Citation
Virtual ant laboratory ( VAL ) model

Material Information

Title:
Virtual ant laboratory ( VAL ) model
Creator:
Alsultaan, Heba
Place of Publication:
Denver, CO
Publisher:
University of Colorado Denver
Publication Date:
Language:
English

Thesis/Dissertation Information

Degree:
Master's ( Master of science)
Degree Grantor:
University of Colorado Denver
Degree Divisions:
Department of Computer Science and Engineering, CU Denver
Degree Disciplines:
Computer science
Committee Chair:
Alaghband, Gita
Committee Members:
Altman, Tom
Stilman, Boris
Biswas, Ashis

Notes

Abstract:
Collective animal behavior plays a major role in optimization decision making. Making decisions in a complex environment like an ant colony provides significant solutions for many problems. Ant colony employs individuals who have limited ability and rules of behavior, but together make a complex system. In this thesis, the Virtual Ant Laboratory (VAL) model is introduced to simulate ants’ foraging behavior. It has two options: closed and open arenas to simulate the ants’ environments and their behaviors, including food consumption as well as communication. As an interactive simulation and prediction tool VAL provides the ability to generate customized complex ant environments. The can determine additional features of the environment, such as food quality and quantity, food source distance to the nest, physical obstacles, adjustable pheromone evaporation and diffusion rates, and varying agent (ant) populations. The model shows that the quality of the food and the distance between the nest and food sources have a significant impact on the density and distribution of the pheromone and, thus, subsequent decisions by the ants. As a result, each ant in the colony will gain the ability to reach their goal by choosing the nearest food. Sharing information by the trail presents a fascinating technique to analyze, improve and/or present new solutions for many problems in complex systems.

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University of Colorado Denver
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Auraria Library
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Copyright Heba Alsultaan. Permission granted to University of Colorado Denver to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.

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Full Text
VIRTUAL ANT LABORATORY (VAL) MODEL
by
HEBA ALSULTAAN B.S., University of Thi Qar, 2010
A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Master of Science Computer Science Program
2017


This thesis for the Master of Science degree
by
Heba Alsultaan has been approved for the Computer Science Program by
Gita Alaghband, Chair Tom Altman, Advisor Boris Stilman Ashis Biswas
Date: December 16, 2017


Alsultaan, Heba. (M.S., Computer Science Program)
Virtual Ant Laboratory (VAL) Model Thesis directed by Professor Tom Altman
ABSTRACT
Collective animal behavior plays a major role in optimization decision making. Making decisions in a complex environment like an ant colony provides significant solutions for many problems. Ant colony employs individuals who have limited ability and rules of behavior, but together make a complex system. In this thesis, the Virtual Ant Laboratory (VAL) model is introduced to simulate ants’ foraging behavior. It has two options: closed and open arenas to simulate the ants’ environments and their behaviors, including food consumption as well as communication. As an interactive simulation and prediction tool VAL provides the ability to generate customized complex ant environments. The can determine additional features of the environment, such as food quality and quantity, food source distance to the nest, physical obstacles, adjustable pheromone evaporation and diffusion rates, and varying agent (ant) populations. The model shows that the quality of the food and the distance between the nest and food sources have a significant impact on the density and distribution of the pheromone and, thus, subsequent decisions by the ants. As a result, each ant in the colony will gain the ability to reach their goal by choosing the nearest food. Sharing information by the trail presents a fascinating technique to analyze, improve and/or present new solutions for many problems in complex systems.
The form and content of this abstract are approved. I recommend its publication.
Approved: Tom Altman


DEDICATION
I dedicate this work to my husband, mother, my family and all real friends who supported me throughout my life and motivated me to challenge the difficulties to do the best. Also, I want to dedicate this work for the special person who gives me the hope and always tells me one day all the earth will be a nice world to live in.
IV


ACKNOWLEDGMENTS
This work was supported by the Higher Committee of Education Development in Iraq. I want to thank them for giving me this great opportunity to finish my master’s degree in the United States. Special thanks to my advisor, Professor Tom Altman. Without his invaluable advice and guidance throughout the thesis work, I wouldn’t have achieved my goals. I also want to thank Professor Greene for his great help with the experimental work and his group for their kind support and advice. Mrs. Raghad Alqurashi participated in Chapter 3 of this project.
v


TABLE OF CONTENTS
CHAPTER
I. INTRODUCTION......................................................1
1.1 Making Decisions...............................................1
1.2 Foraging.......................................................1
1.3 Pheromones.....................................................2
1.4 VAL............................................................4
II. COMPLEXTY AND AGENT BASED MODEL..................................7
11.1 Agent Based Model.............................................7
11.2 Complex System................................................8
11.3 Netlogo.......................................................9
III. IMPLEMENTATION OF VAL...........................................11
111.1 Purpose....................................................11
111.2 VAL Entity, State Variables and Scales......................11
111.3 Model Process Overview and Scheduling.......................13
111.3.1 Ants’ Movement (VALo):.................................13
111.3.2 Ants’ Movement (VALc):.................................14
111.4 Design Concepts.............................................15
111.4.1 Emergence:.............................................15
111.4.2 Adaptation / Fitness:..................................15
111.4.3 Prediction:............................................15
111.4.4 Sensing:...............................................16
111.4.5 Interaction:...........................................16
VI


111.4.6 Stochasticity:..............................................16
111.4.7 Collectives:................................................16
111.4.8 Observation:................................................16
111.5 Modal Initialization..............................................17
111.6 Sub-Models........................................................18
IV. Evaluation Methodology / Results and Analysis...........................21
IV.1 Real Experiment / Experimental Setup...............................21
IV. 1.1 Results.....................................................22
IV.2 Simulation.........................................................23
IV.2.1 Experiment I: Food Distance vs. Food Consumption Time and Number
of Ants...................................................23
IV.2.2 Experiment II: Food Quality vs. Food Consumption Time and Number of Ants Carrying Food...............................................25
IV. 3 Statistical Analysis..............................................27
IV.3.1 Statistical Analysis for Experiment 1:.......................27
IV.3.2 Statistical Analysis for Experiment 2:.......................33
V. CONCLUSION..............................................................37
V. 1 The Model and Results.............................................37
V.2 Further Research....................................................38
REFERENCES..................................................................39
APPENDIX....................................................................42
Appendix A. VALc Code...................................................42
Appendix B. VALo Code...................................................56
vii


LIST OF FIGURES
FIGURE
Figure 1.1 Stages of the pheromone life.................................4
Figure 1.2 Main diagram for the movement of an ant in the VAL model.....6
Figure 11.1 The main components of the ant colony.......................9
Figure IV.1 Real experiments setup.....................................21
Figure IV.2 Real experiments to show the differences among 3 food sources after 20
minutes......................................................23
Figure IV.3 Distance vs. food consumption time in VALc.................30
Figure IV.4 Distance vs. number of worker in VALc......................31
Figure IV.5 Distance vs. food consumption time for VALo................31
Figure IV.6 Distance vs. number of ants for the VALc...................32
Figure IV.7. Quality of the food vs. consumption time for VALo.........34
Figure IV.8 Quality of the food vs. consumption time for the VALc......35
Figure IV.9 Quality of the food vs. number of ant in each box in VALc..36
viii


LIST OF TABLES
TABLE
Table 111-1 State variables used to describe model entities.................13
Table MI-2 Lists the main set of required parameters........................17
Table IV-1 The input for VALc when the distance between the nest and food sources is changed..................................................................24
Table IV-2 The input for VALo when the distance between the nest and food sources is changed..................................................................25
Table IV-3 The input for VALc when we change the quality of the food is changed.
.................................................................26
Table IV-4 The Input for VALo when the quality of the food sources is changed.... 27
IX


CHAPTER I
INTRODUCTION
1.1 Making Decisions
Many decentralized self-organized natural systems are known for their cooperative behaviors that include collective decision-making, where groups of agents can make optimal choices without perfect environment knowledge or leadership [1], Social insect colonies have significant abilities to choose the best action among several routes of action, since they make a decision based on obvious knowledge shared among individuals. In ant colonies, each ant has limited local information and appropriate rules to make decisions. An ant collects information and makes decisions to benefit the entire colony. The main task for each worker is foraging for prey. Each individual ant (agent) collects information from the environment to create that colony’s foraging knowledge. The ants are making their decisions based on this knowledge that is reinforced by leaving positive feedback (a pheromone) on their way back to the colony with the food; thus, his local information benefits the colony as a whole [2-3].
1.2 Foraging
Various biological foraging strategies such as pheromone recruitments, nest recruitment, and random search have been noted [4], Pheromone recruitment is one of the significant foraging strategies in the right environment. Computer scientists have been employing pheromone foraging behaviors as an inspiration for ant colony optimization algorithms (ACO) [5]. These algorithms utilize pheromone recruitment
1


strategies to solve complex computing problems [6]. Foraging styles differ significantly according to food type. For example, the worker ants will focus their activity on high quality sucrose source, as opposed to lesser quality protein-based sources [7],
In this thesis, we will focus on models simulating pheromone recruitment strategies. VAL employs pheromone recruitment to model various ant lab environments. After they find food, the ants communicate by depositing a pheromone trail on the way back to the home. Flence, they make trails between their colony and a food source. Other ants follow these pheromone trails when they find them. As a result, these trails provide the colony with nearly optimal foraging capabilities.
1.3 Pheromones
A Pheromone is a chemical compound that is discharged from specific glands. Ants use pheromones as a method of indirect communication. Foraging through pheromone trials simply represents a faster way to recruit workers by utilizing chemical components from the Dufour gland which has an attracting role [8]. In the beginning, ant forage for food sources in all directions around the nest. Two main roles are recognizable when the foragers are looking for food/prey: first, the ant workers walk randomly (see Figure 1.1.A); second, if they find the trail, they respond to the pheromone according to Weber’s Law to determine the turning angle [9], which means following the high concentration trail. Communication by trail is most helpful when the forager detects a food source exploited by more than a single worker [10]. Therefore, when the worker finds the best food, it shares the information about the food’s location with the colony members to redirect them to the food (see Figure 1.1 .B). The worker will draw its trail by depositing pheromones along the path between the
2


food source and the nest. Other ants will follow this pheromone trail when they sniff it (see Figure 1.1 .C). Each agent will redraw the trail when they carry food to their home, thus changing the pheromone concentration [11], Trail intensity is affected by the type of the food source and the distance between the food source and the nest.
In the beginning of the search task, the pheromone concentrations at all positions will be zero, then they will change at different times (see Figure I.1.D). Second, the pheromone will encourage other workers to follow it to reach to the food. After the first pheromone trail is discovered, the number of ants which follow this trail will increase significantly. As a result, an increasing number of ants carrying the food will reinforce the trail. Flowever, when the food source is gone, the ants go back empty and don't leave a trail (see Figure 1.1 .E). The pheromone will evaporate with time (see Figure I.1.F). Because the trail is ephemeral, this will give the workers a good opportunity to search for new food/prey [12], In conclusion, we could say that ants indirectly communicate by laying pheromones on the ground when they have discovered a food source. Moreover, the trail concentration in a particular position has a positive correlation with the number of ants that have already passed that position. The ants respond to the pheromone and use it as a guide to reach their goal.
3


Figure 1.1 Stages of the pheromone life.
(A) Ants randomly foraging for the food. (B) The forager finds food and lays a trail. (C) Ants deposit pheromones along the way between the food and nest. (D) Ants reinforce the trail and the number of ants increases. (E) Ants don’t leave pheromone because the food is gone. (F) The pheromone is evaporating.
1.4 VAL
The Virtual Ant Laboratory open and closed, (VALo and VALc) models can simulate ants’ environment with and without barriers, respectively, thus allowing the simulation of ants’ behaviors in fundamentally different environments. The main goal of this model is to improve an agent-based model for ant foraging and study how the ants make decisions when they are choosing among food sources as a function their quality and distance from the nest. Also, the VAL model was developed to be used in conjunction with biology ant labs to simulate ants’ behavior and reduce the cost of the actual lab experiments. Our model could be used by students to conduct their
4


experiments in studying the ants’ behavior in different cases and situations, rather than performing costly lab materials that use up time and other resources.
Our model was developed using the modeling program Netlogo 5.3.1, see [16], which will be discussed later in this thesis. The agents (ants) and two environments were created: an open arena is a real-world scenario (the ants could move anywhere outside of the nest) and a closed environment (the food sources and ants are inside boxes) and the ants can move only inside the boxes that are connected by tubes. In both cases, the ants are either looking for food or carrying it back to the nest. VAL incorporates many parameters that can significantly affect the behavior of ants. Figure
1.2 shows the main flow diagram for movement and action responses in VAL.
5


Figure 1.2 Main diagram for the movement of an ant in the VAL model.
6


CHAPTER II
COMPLEXTY AND AGENT BASED MODEL
11.1 Agent Based Model
Sometimes we wonder, when we see a group of animals act in beautiful emergent patterns, if they have a central leader and/or know exactly where to be or what to do at the same time, or if they act based on the local information alone. An agent based model may be used to help us with these questions. It is a creative way to go from individual rules of behavior to the aggregate emergent actions. An agent based model multi tasks as if exploring some theory and understanding of how some elements relate together to form a creative pattern or even create a complex entity.
In general, the model represents an abstracted description of a process, object, or event. Also, the agent in a computer simulation is an autonomous individual element with its own properties and rules for actions. Furthermore, an Agent-Based Model (ABM) is modeling the agents and their environment, while describing the interactions between agent-agent and agent-environment. An ABM will provide a deeper understanding of how a large number of interacting parts, which evolve over time and work together, e.g., the interplay within an ant colony or an urban traffic jam.
Agents have simple rules and behave almost independently. Then, they will provide feedback to affect those nearby individuals trying to make a decision. As a result, they come together to create evermore complex systems. Each agent makes a decision based on its local neighborhood to create complex patterns and/or structures in nature or society. Thousands of individual system elements could be
7


simulated by providing the agents with the basic rules of behavior and providing us new ways to study complicated phenomena in which these agents are the key players.
VAL utilizes an agent-based model to study the effectiveness of pheromone foraging strategies in lab environments, focusing on the distance between food and the nest, and quality of the food. Our model was developed using the modeling program Netlogo 5.3.1, a multi-agent programmable modeling environment that was especially designed for such simulations.
II.2 Complex System
Complex systems are composed of many complicated parts with nontrivial interactions. Each part could be a subsystem with a its own hierarchic structure. Decomposition is a basic technique used to divide complicated components into resolvable and manageable parts. Therefore, a scientist should begin by studying the characteristics of the subsystem, because it easier to observe and analyze. Then, one should define how the small chunks interact with each other in order to recognize functionalities for the parent system. Understanding how the small parts work and interact more often than not helps us find the solution to more complicated problems.
Globally, system simulation and modeling presents a powerful way to represent complicated connections and/or interactions. The main reason to use an agent based model are deeper understanding of a given complex system and, perhaps, solving a problem not solved before. For example, an agent based model can be employed to investigate the role of the pheromone in recruiting additional foraging workers in ant colony [13]. It could also be used to demonstrate how to utilize the ant colony’s strategy model for finding solutions to very complex problems, e.g., finding the
8


shortest Hamiltonian tour in a graph to solve traveling salesman problem (TSP) [14].
Simulation provides structure which helps to understand and solve complex problems in systems [15]. For our purposes, there is a need to define the concept of an agent based model (agents and environment) and its interacting subsystems.
The ant colony system was broken into its many parts, as shown in Figure (11.1). The VAL model was designed by taking into account its main characteristics as well as its many subsystems, which were identified here and found by other researches. Finally, the relationships between the small parts of the complex system were defined and modeled.
i i * i

| Ants I How the agents interct with each other I How the agents interct with the environment I Environment
i i 1
| Pheromone 1 Foraging [ strategies 1 | Making decision 1 Open | arena 1 Inside 1 boxes
II.3 Netlogo
Figure 11.1 The main components of the ant colony
Netlogo is a multi-agent modeling language and development environment. It was designed by Uri Wilensky at Northwestern University. It is one of the most widely used languages for these purposes and it is a powerful platform for scientific modeling in general [16]. The environment has three core agent types: turtles, patches, and links. Netlogo can accommodate modeling and/or simulations for thousands of agents
9


which behave in parallel (moreover, usually nondeterministically) within complex systems. The language describes the interaction and the accumulation of thousands of components that have a characteristic behavior. Netlogo has a site which provides the free version to download, help, and a huge library of models [17]. In addition, it supplies excellent sample models of how an ant colony behaves and the role of pheromone [18-19].
Netlogo can be used for the modeling of a complicated system for many reasons. First, Netlogo is open source; therefore, programmers can access and edit its origin code. More, it provides a graphical interface for “Inputs”, a widget that provided the users with the ability to enter the exact value of a global variable or parameter and easily changed, and “Outputs” to see the interaction with the agents, based on the input. Moreover, it supports the users with a “speed controller” to let them determine how often the display is updated. It also supplies the scientists with an amazing tool called the “Behavior Space” to analyze their experiments with multiple runs, since it allows the users to run an experiment on different processors, but results are all collected into one Excel output file.
A complex model was introduced to test the relationship between the distance of food sources from the nest and the quality of the food, and the ants’ reaction on a larger scale. An agent based model was set-up using the Netlogo modeling environment. Two VAL models were installed: Virtual Ant Laboratory open arena and Virtual Ant Laboratory closed boxes. The user controls the parameters to create a specific environment and the corresponding results. VAL’s description follows the standard ODD (Overview, Design concepts, Details) protocol [20-21],
10


CHAPTER III
IMPLEMENTATION OF VAL
111.1 Purpose
The model was developed to simulate a complex system (an ant colony system) to explore the ants’ behaviors as a function of their food source’s distance from the nest location as well as the food source’s quantity and quality. VAL simulates the ants’ behaviors in two environments: inside an ant laboratory boxes (VALc) and on open ground (VALo). The main goal of this thesiss’ is to construct an agent based model for ant foraging and study how the ants make decisions when presented with various surroundings. The VAL model was developed to simulate an actual ant laboratory in order to analyze ants’ behaviors, while reducing the cost of actual lab experiments. Researchers can conduct experiments under various configurations and/or situations, thus allowing them to propose (or reject) hypotheses regarding ants’ behaviors.
111.2 VAL Entity, State Variables and Scales
The model defines the following entities:
Ants - The main agents in the system. Graphically the ants are identified with a red color when they are looking for food, and the ants carrying food back to nest are colored yellow.
Food sources - The food consumed by ants. There are several food sources that may be introduced in the simulations. The ants can consume one or more of the
11


food sources that are represented as a cluster of patches. Each time an ant carries away some food, the cluster, which represents the food source, will decrease by one patch in size. Each food source has three features:
1. Food Quality: different food quality levels for each food source. The ants have the capability to determine the specific food source quality.
2. Food Quantity: the quantity feature of the food source determines the number of patches (squares) in the specific cluster representing that particular food source.
3. Food source position: the location, including the distance, between each food source and the ants’ nest.
Food Source Boxes - square boxes around the nest and food sources. There are three sizes of boxes: large, medium, and small.
Tube - rectangular tubes are used to connect the boxes of the food sources to the nest box. The ants can only move from/to the food source through the tubes with a maximum of 2 ants in the tube at a time at a specific point; each tube has a total capacity of 30 ants. The tube length varies based on the position of the food source box in the simulation world. The tube in our models is a Netlogo built-in link that connects two nodes and its displayed thickness is variable. In this model, these links have visualized thickness, or width, that range from 0 to 5. Table 111-1 lists the state variables used to describe each entity.
12


Table 111-1 State variables used to describe model entities.
Entity State variable State variable description
Ants Population From 2 to 600 ants in VALc environment and from 2 to 10000 in VALo environment.
Color Red for the ants who are looking for food, and yellow for the ants carrying food back to the nest.
Food sources Number of food sources Two to three food sources that can be present in any single simulation.
Quality Six different food quality levels for each food source.
Quantity Number of patches (squares) in a given food source cluster.
Position The (Euclidean) distance between the food sources and the nest - Coordinates.
Food Source Boxes Sizes Surface area.
Tube Width Fraction of patch size (for visual display).
III.3 Model Process Overview and Scheduling
The model defines the following processes:
III.3.1 Ants’ Movement (VALo):
The ant first randomly walks in the area in order to find a food source. The maximum turning angle is + / - 45 degrees. When the ant finds the food, it carries a piece of the food from the source and goes back to its nest while laying a pheromone trail on its way back. At this time, the ant’s color will be shown as yellow, and the amount of food will decrease by one. The ant follows the “nest scent” to find its way back to the nest. When other ants smell the pheromone, they follow it to the food source. The process is that the ants sniff within 45 degrees left and right, and go in the direction of the strongest smell. If the ant does not sense any pheromones, we expect it to continue moving randomly. As more ants move the food to their home,
13


they increase the pheromone trail. The ants orderly exploit the food sources if we assume food quality is equal. They begin with the nearest food to the nest and finish with the food source most distant from their home.
III.3.2 Ants’ Movement (VALc):
In the lab, researchers use materials such as boxes and tubes to conduct experiments. VALc supports a flexible virtual environment since it provides these tools with different options. First, virtual square boxes are added around the nest and food sources. Second, the tubes are added (with specific width and capacity) that connect the boxes together. The ants can only move from the nest to the food sources through these tubes. An ant first randomly walks only inside the nest box to find a tube to a food source. It moves randomly 45 degrees left or right and, when it finds the tube, it gets into it to leave the box, and search for food. When the ant finds the food source, it holds a part of the food and return to the nest and (in the simulation) its color will change to be yellow. The ants will find a tube to return to the nest. After the ants reach the nest box, they will smell left and right and redirect where the strongest smell to their home is. It can be said that, in both VALo and VALc, ants will either move randomly or follow a high concentration of the pheromone if/when they find a trail.
14


III.4 Design Concepts
111.4.1 Emergence:
The following phenomena emerge from the interaction between the individual ants and the food sources and their variables.
Food consumption: Amount of each food source that is taken/consumed by the ants during each time step.
Ants’ box distribution: The number of ants in each box during each time step. Amount of time ants spend foraging: The total amount of time (in seconds) the ants spend foraging; i.e., the simulation time.
Number of carrying ants: Number of ants carrying food during each time step. Amount of time before reaching food: The elapsed time (in seconds) from the start of the simulation until the first ant reaches any food source.
111.4.2 Adaptation / Fitness:
It is assumed that the ants’ behavior would optimize individual fitness. During the simulation, ants select the pheromone trail that maximizes their perceived behavior fitness.
111.4.3 Prediction:
The future ants’ behavior and decisions can be predicted by using the sub models and observing the behaviors over previous time steps.
15


111.4.4 Sensing:
In this model, the ants sense pheromones within 45 degrees left and right, and go toward the strongest smell. If an ant does not discover the pheromone, it will move randomly. Also, the ants sense the nest on their way back by sniffing left and right and follow the strongest smell to locate their nest.
111.4.5 Interaction:
Ants can only interact with each other indirectly through the pheromones. By increasing the number of ants in the simulation, we can increase the consumption rate.
111.4.6 Stochasticity:
The ants’ movements while foraging is a stochastic event.
111.4.7 Collectives:
Collectives are included in the model. Collectives are represented, as each individual behaves independently from another, but its behavior impacts the overall colony decisions.
111.4.8 Observation:
During the simulation, all state variables can be presented. Also, all data is saved in files during each time step (upon initial user request). Data and simulation steps can be saved as a spreadsheet file, and the world can be exported to a common graphical file, e.g., JPEG or PNG. The model can be run many times, systematically
16


varying the model's configurations and recording the results. Repetitions, reports (behaviors of interest), stop condition, and the time limit of multiple runs is determined by the user.
III.5 Modal Initialization
The initial values of state variables are either read from input parameter files and, as shown in Table 111-2, or they are calculated from state variables based on given equations. They can be either single values, or an equation (sub model) to calculate their values during each time step.
Table 111-2 Lists the main set of required parameters.
Entity Parameter Description
Ants Population: Number of ants in the simulation. Slider range from 2 to 600 in VALc environment, and from 2 to 10000 in VALo environment.
Food sources Number of food sources (2 or 3) Switch: if the switch is on, there will be 3 sources in the world; otherwise there will be 2 sources. A different color is utilized for each food source to make it easy to follow and control them.
Quality Slider range from 0 to 1 and the increment rate is 0.2. Also, shades of color are used to represent different food qualities.
Quantity Slider range from 100 to 500 units and the increment rate is 10.
Position Slider range from 30 to 60 in VALc environment, and from 10 to 70 in VALo environment with the increment rate is 10 in both environments. It is the number of patches that represent the distance between the food sources and the nest that is at the origin (0,0).
Food Source Boxes Sizes List of three choices: large (12), medium (9), small (6).
Tube Width Slider range from 0 to 5 (for visual display).
17


III.6 Sub-Models
First, the user defines how the agent behaves after leaving its nest. The worker ant will use pheromones as a foraging method in the VAL model. The forager could follow a pheromone trail by walking towards the direction of the patch with the most pheromones or walking (±45°) randomly in case it does not sense the pheromone trail. Pheromones are composed of chemical materials inside the Dufour gland. When the ants carry food, they leave a pheromone trail to inform other ants.
Pheromone: In the proposed model, the pheromones increase by the value of 60 every time an ant returns back to the nest. That is, at a given location,
Pheromone = Pheromone + 60 (1)
The pheromone has a scale-color green that is affected by an evaporation rate, the distance between food boxes and nest, diffusion rate, and the quality of the food, as shown below:
Evaporation: when the pheromone evaporates, its scent reduces and thus the number of ants following that pheromone trail will decrease. The main advantage of evaporation is avoiding convergence to a locally optimal solution. The pheromone evaporates exponentially with time. In this model, the evaporation rate of a pheromone is in the range from 0 to 99, i.e.,
Pheromone = Pheromone * (100 - evaporation-rate) /100. (2)
Diffusion: Diffusion means each patch gives equal shares of a (diffusion rate) percent of the value of pheromone to its eight neighboring patches. The diffusion rate is a required input, which is a slider range from 0 to 99, hence,
Diffuse pheromone = (diffusion-rate /100). (3)
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Density: Pheromone concentration at given location has more density when it gets marched over more frequently. In other words, as more ants carry food to the nest, they will reinforce the pheromone and more ants will follow this trail.
Food source quality: The following assumption was made about the increment ratio of the pheromone based on the chosen quality:
Pheromone = Pheromone + (Quality * 100). (4)
The slider is used to control the food quality with a range from 0 to 1 and the increment ratio is 0.2. Shades of color are used to represent different quality levels. The range of color that represents different quality for first food source:
0 0.2 0.4 0.6 0.8 1
low quality............. high quality
The range of color that represents different quality for second food source:
0.2 0.4 0.6 0.8
ow quality............. high quality
The range of color that represents different quality for third food source:
0 0.2 0.4 0.6 0.8 1
low quality .... high qua
ity
The distance between food source and nest: in general, the VAL model has the world with length of 160 patches and width of 160 patches. The x cardinalities are from - 80 to +80 and the y cardinalities are from - 80 to +80 (patches). The position of
19


the nest is at the origin (0,0), which is in the middle of the virtual world. In the VALc model, there are four distances for each food source from the nest: 30, 40, 50, or 60 patches. In the VALo model (where there are no boxes) there are seven distances for each food source from the nest: 10, 20, 30, 40, 50, 60, or 70 patches.
20


CHAPTER IV
Evaluation Methodology / Results and Analysis
IV.1 Real Experiment / Experimental Setup
We performed a real experiment in Professor Greene’s biology lab at the University of Colorado Denver campus. He utilized 300 pavement ants to do his experiments. In the laboratory, 12 X 18cm a plastic rectangular tray was used as an open area and circular platform 60 X15mm as a nest. The experiments were repeated, each with 100 pavement ants put in the circular nest. The sucrose solution was made by mixing water and 20mg of sucrose, then placed on 0.055 mg cotton to supply the ants with three food sources. The foods were placed 6,12, and 18cm away from the nest entrance, see Figure IV.1. All experiments were filmed with a high definition video camera (Canon HDD zoom 6.1-61 mm, Sony HDR-XR520, TOSHIBA 4.4-52.8mm, SAMSUNG HD 2.1- 109.2 mm) to analyze whether the distance could have an effect on the number of ant in each food source.
St *r

" l * . 1 / / *
Figure IV.1 Real experiments setup.
21


IV. 1.1 Results
To analyze the effect of distance on decision making by the ant in choosing its food source, the results of real experiments were recorded. Unfortunately, video records (data) of only one experiment were captured.
In the beginning, the workers foraged randomly in the area looking for the food. Then, the ants started to form trails that lead to the food source. Other ants followed the pheromone trail to reach the food. After the workers found the sucrose solution, they stood underneath the feeder to drink the syrup.
Data (number of ants in each food source) was collected each minute. The ant found the first food source in the first minute of the experiment, the second food after five minutes, and the third one in nine minutes. Through time it was noted that the first food source attracted a significant number of the ant. The number of the ants (during the 20 minutes of the experiment) in the three food sources reached 16, 8, and 4 ants respectively, 29, 19, and 15 ants respectively for the next 20 minutes, and 33, 21, and 14 ants after 60 minutes. Then, the number of ants decreased, because the food source was consumed. The ants went back to look for new food/prey in a random fashion. We could conclude that the workers increased their foraging effort toward the source when it had the smallest distance from the food nest, see Figure (IV.2). When we performed a Correlation test on the output of the experiment, the results show as (R= - 0.547012386050654). There is a significant negative correlation between the distance and number of ants. This means that when the distance decreases, the number of ants that recruit to the food source increases.
22


Figure IV.2 Real experiments to show the differences among 3 food sources
after 20 minutes.
IV.2 Simulation
IV.2.1 Experiment I: Food Distance vs. Food Consumption Time and Number of Ants
To determine whether the number of ants which carry the food and the consumption of each food source based on distance to the nest was significantly different, experiments we completed in both VALc and VALo.
The first experiment in VALc, the environment was built with variables as shown in Table IV-1. This experiment was performed 60 times. Then the distance between food source one and the nest was changed. Initially, the distance was 30 patches and we increased the distance 10 at a time until 70 patches were reached. These experiments were duplicated 12 times for each change in distance. The following data was collected: time of the food consumed, number of ants which carry food, and time.
Second, this experiment was repeated in VALo with the same input as presented in Table IV-2. The nest and food sources were presented in the open area.


The experiment was run 96 times with 12-time duplications for each change in the food-distance from the nest. We began with 10 patches incrementing by 10 patches until a 70 patches distance was reached. The following data was collected: the number of yellow ants (which carry food), consumption of the food, and time.
Table IV-1 The input for VALc when the distance between the nest and food
sources is changed.
Input Description
population 100 ants
Diffusion -rate 35%
Evaporation-rate 35%
Add food source 3 On
Foodl-position From 30 to 70 with increments of 10
Foodl-quantity 500 units
Foodl- quality 0.2
Food-source1-box size Medium
Food2-position 40 or 50 patches away from the nest
Food2-quantity 500 units
Food2- quality 0.2
Food-source2-box size Medium
Food3-position 60 or 70 patches
Food3-quantity 500 units
Food3- quality 0.2
Food-source3-box size Medium
Nest- box- size Medium
Tube- width 5
24


Table IV-2 The input for VALo when the distance between the nest and food
sources is changed.
Input
Description
population 100 ants
Diffusion -rate 35%
Evaporation-rate 35%
Number of food sources 3
Food 1-position From 10 to 70 with increments of 10
Food 1-quantity 500 units
Foodl- quality 0.2
Food2-position 40 or 50 patches away from the nest
Food2-quantity 500 units
Food2- quality 0.2
Food3-position 60 or 70 patches
Food3-quantity 500 units
Food3- quality 0.2
IV.2.2 Experiment II: Food Quality vs. Food Consumption Time and Number of Ants Carrying Food
Similar to the experiment above and also to see the effect of the food quality on the ants’ decision to move in a specific direction, the number of ants in all food boxes and the time require to consuming each feeding source in both VALc and VALo was monitored.
In the beginning in VALc, the environment was built with variables defined as shown in Table IV-3. The quality for the first food was gradually changed from 0 to 1 with an increasing rate of 0.2, and the second food quality was fixed at 0.2. For each food quality value, the simulation was run 12 times, thus the simulation was run 72
25


times for the whole test. Finally, all the data about the time of the consumption of the food, number of ants which carry food in each box, and time was gathered into one file for analysis.
Next, the experiment in VALo was performed with the same inputs presented in Table IV-4. The experiment was run 72 times with 12 time duplications for each change in the food- quality. We began from 0 with 0.2 increments until 1 was reached. The following data points were monitored: yellow ants (which carry food), consumption of the food time, and ticks.
Table IV-3 The input for VALc when we change the quality of the food is
changed.
Input Description
population 100 ants
Diffusion -rate 35%
Evaporation-rate 35%
Add food source 3 OF
Foodl-position 30 patches away from the nest
Foodl-quantity 500 units
Foodl- quality From 0 to 1 with 0.2 increment
Food-source1-box size Medium
Food2-position 30 patches away from the nest
Food2-quantity 500 units
Food2- quality 0.2
Food-source2-box size Medium
Nest- box- size Medium
Tube- width 5
26


Table IV-4 The Input for VALo when the quality of the food sources is
changed.
Input Description
population 100 ants
Diffusion -rate 35%
Evaporation-rate 35%
Number of food sources 2
Food 1-position 0.2
Food 1-quantity 500 units
Foodl- quality From 0.2 to the 1 with 0.2 increment
Food2-position 20 patches away from the nest
Food2-quantity 500 units
Food2- quality 0.2
IV.3 Statistical Analysis
In general, the purpose of developing a simulation model is to collect data to analyze and to gain insights into the system. In the VAL model VAL, the data is collected automatically by using a special tool called behavior space. A statistical test was conducted in both VALc and VALo.
IV.3.1 Statistical Analysis for Experimentl:
Since, there are multiple distances in the VAL, three of them 40, 50 and 60 were considered as independent variables. Three types of tests and graphs are presented to show how changing the distance has a significant effect on the consumption of food and number of ant carrying food.
First, the ANOVA test [22-23] was conducted. The variation was tested within and between groups because the ants in each group have different reaction times. In this test, the first box was in the distance “40”, the second box in the distance “50”,
27


and the third one in the distance “60”, away from the nest. The null hypothesis means no difference between the number of ants in all boxes. The alternative hypothesis is that there is a difference between the number of ants in the boxes. That means at least one of the boxes is different than the rest if there is success in rejecting H0. The results of the ANOVA test show that there is no clear difference between three boxes in the number of ants and the consumption time when the space between food source is 10 patches. However, there is significant effect on the consumption of food time when the gap among the three food sources is 20 patches according to the analysis of the output for VALo. The results of the three-different parameters of the distances 30, 50 and 70 are F= 21.62540953, p-value= 9.96427E-07, Fcrit= 3.284917651. It demonstrates that F > p, which means rejecting H0 and Fcrit represents that any F value > than Fcrit could reject H0. Moreover, ANOVA tests on the number of workers illustrates that there is a great difference among tree food source boxes in VALc. The null hypotheses was rejected, since F= 15.68044521, p-value= 1.69E-07, Fcrit= 2.999018396. As a result, we could say that the distance of the food sources from the nest has a huge effect on both the numbers of ants which carry food and the consumption time of the food because we reject H0 in both cases when the gap between the food sources is 20 patches.
Second, to see which distance has most significant effect than others, Post Hoc Test [24-25] was applied. The result shows that the food source numberl is different than other sources but the difference is less than 95% when the gap between the sources is 10 patches. Figure VI.3 explores the role of the three-different distances on the food exhaustion time. The experiment was done on VALc with 12 duplications.
28


The three food boxes located 30, 40 and 50 patches away from nest box. There is no clear impact of the distance on reducing consuming time in 7 of 12 duplications when the space among food sources is 10 patches. Figure VI.4 displays the impact of the distance and how it affects the number of ants which carry food in each food box. The experiment done on VALc with 12 duplications. The three food boxes located in 30, 40 and 50 patches away from the nest box. There is no clear impact of the distance on an increase of the number of workers which carry food in the first part of the simulation when the space among victim foods equals 10 patches. This is in contrast to the other results from the analysis of the data from the first experiment when the distance among food is 20 patches or more. Post Hoc Test indicates a significant difference between the nearest food source and the others in number of ants in the boxes and the time of food consumption when the space between food sources is 20 patches or more. The test for the consumption time on VALo illustrates that there is a huge difference, since the value of p =0.000078 between food source 1 and 2 with distance 20 and 40 and the value of p= 0.000006 between food 1 and 3 with distance 20 and 60. Even though there was some difference in the mean between the second and third source, there was no significant difference between them, since the p value was 0.364761316. Figure VI.5 displays the effect of the distance on the food consumption time. In the experiment done on VALo with 12 duplications, the three food sources were located 20, 40 and 60 patches away from nest. A clear impact could be seen as a result of the distance of the first food over others on decreasing the consumption time in 11 of 12 duplications when the gap among food equals 20 patches. Moreover, the test on VALc with a gap of 20 patches among three food
29


sources shows significant difference in the number of workers in each box, because of p= 0.000831789 between food 1 and food 2 with distances 30 and 50 and p= 0.00000014 between food 1 and food 3 with distance 30 and 70. The result was less significant in the number of ants in box-2 and box-3 while p= 0.016913809. Figure IV.6 shows the impact of the distance and how it impacts on the number on ants which carry food in each food box. In the experiment done on VALc with 12 duplications, the three food boxes were located 30, 50 and 70 patches away from the nest box. A clear impact could be seen as a result of the distance on increasing the number of workers which carry the food in the first part of the simulation. The blue line represents the number of ants which carry food in box-1 and how the number decreases after the time of 300, because the amount of food decreases when the space among prey food equals 20 patches
Distance VS. Food Consumumption Time
7000
â–  Foodl with distance 30 from the nest â–  Food2 with distance 40 from the nest
â–  Food3 with distance 50 from the nest
Figure IV.3 Distance vs. food consumption time in VALc.
30


Food Distance VS. Number of Ants When The Gap is
10 Patches Between Food Sources
20
number of Ants in boxl ^^“AV number of Ants in box2 ^^“AV number of Ants in box3
Figure IV.4 Distance vs. number of worker in VALc.
Distance VS. Food Consumtion Time
7000
6000
â–  20 140 160
Figure IV.5 Distance vs. food consumption time for VALo.
31


Food Distance VS. Number of Ants When The Gap is
20 Patches Between Food Sources
^^i^oro^crirsjLnco^i'^-r^oro^crirsjunco^i'^-r^ornLDcrirsjunco^H'^-r^oro^cri
rM^[^cn^nn^ooonnLnr''.orNi'3-LOcr>s-innu3ooonr)Lnr''.cr>rNi'3-LOcr>^H(r)LOooorM
^^^H^Hrsjrsjrsjrsjnnnnnnnnnn'^-'^-^t'^-LnLnLnLnLniJDiJDiJDiJDr^.r^r^r^oooo
# of ants in box-1 # of ants in box-2 ^^—AV # of ants in box-3
Figure IV.6 Distance vs. number of ants for the VALc.
Third, to find out if there is a relationship between the time of food consumption and the distances between the nest and the foods. A correlation test [26-27] was performed on the data collected from the 84 runs on VALo. The results showed that there is a positive correlation R = 0.43944077 which means that when the distance decreases, the time of food consumption will also decrease. The result shows there is less significant effect when the distance among the sources is 30, 40, and 50, since R is less than 0.5. However, the effect of the distance on the exhaustion of food is significant when the distance is more than 20 patches among them. The result R = 0.69420813 represents a huge effect and a positive correlation. Second, the test on VALc was performed to find out if there is a relationship between the average number of ants in the food sources boxes and the distances between the nest box and the food boxes. The result showed that there is a big negative correlation R = -0.752154165 when the gap among food is 20 patches and more. That means the number of ants inside the food source box decreases as the distance between the
32


nest box and the food source box increases. These results indicate that the ants
consume food in order, beginning with the nearest to the nest of the colony.
IV.3.2 Statistical Analysis for Experiment 2:
While there are multiple values of the food quality, we tested to see if the quality has an effect on redirecting ants to a special food and the time of food consumption. The results which were obtained from both VALc and VALo in experiment 2 were processed. An ANOVA test was completed to see if there is a difference when the quality changes. All of the results obtained from the test cannot reject H0, which means there is no clear difference between food sources quality when the gap between food quality is less than 0.8.
First, the analysis of the second experiment for VALo indicates that quality of the food has a small effect on the consumption time when the difference between quality is less than 0.8. Therefore, the result of the analysis of the two qualities of the food source (0.2, 1, respectively) with 12 duplications is presented. Post Hoc Tests were applied to see which quality is different than other and if the difference is more than 95% or not. Food source two with high quality is different than source one low quality in consumption time, but the variation is not huge as shown by P= 0.04404056. Finally, a correlation test was performed and R= -0.414472333 showed that there is a medium negative correlation, since increasing the quality will decrease the time of food consumption. Figure IV.7 illustrates the time which is needed by the workers to exhaust the two types of foods (food one with a quality of 0.2 quality and food two a quality of one) in 12 runs. Times are measured in ticks (tick= 0.05 seconds). Overall, food one with low quality has higher consuming time than the second source with high
33


quality. Food two had smaller consumption time in 10 of 12 duplications. In contrast to experiments 5 and 7, the source with 0.2 quality had a smaller consumption time. The most time that the ants needed to carry all of food two to the nest is 4000 ticks compared to 5000 ticks for food one. The forgers need 2500 to 4000 ticks to consume all of food source two.
Quality of The Food VS. Consumption Time
6000 5000 4000 3000 2000 1000 0
123456789 10 11 12
â–  Food-1 with 0.2 unit qulity â–  Food-2 with 1 unit qulity
Figure IV.7. Quality of the food vs. consumption time for VALo.
In addition, ANOVA analysis for the second experiment for VALc are the same results as for the VALo. The quality has less effect when the gap between the food quality less than 0.8. Therefore, the result for two food sources with two different qualities 1 and 0.2 respectively is presented. To see which one had more impact on food consumption time, a Post Floe Tests were performed. In this test, the first food source with high quality had a higher effect on the consumption time of the food than another source with less quality, but the impact was less than 95% since P=
34


0.035628175. Moreover, the correlation test shows that the exhaustion time has a
medium negative correlation with food quality. Figure IV.8 demonstrates the 12 runs for experiment two and shows the effect of the two qualities on the time required to move the food to the nest. The food with rich quality (box-1) was consumed faster than the food in box-2. The food in box-1 was totally consumed in less than 250 ticks, while it took more time finish the second box in most runs. The food in box-1 is consumed faster than food-two in eight of twelve duplications.
Food Quality VS. Food Consumption Time
123456789 10 11 12
â–  Food -source 1 with 1 quality â–  Food -source 2 with 0.2 quality
Figure IV.8 Quality of the food vs. consumption time for the VALc
Moreover, we tested the role of the food quality on the number of ants in each box-source in VALc. We conducted both Post Floe Tests as well as the correlation tests. The result of the Post Floe test is less than 95%, since P= 0.053019658. Also, the output of the correlation test shows us that the quality has medium positive effect with number of workers which carry food, since R= 0.40246168. The number of ants in the first food source box was higher in the first period of the simulation than the other box, because the quality of the first food source was higher than the other food
35


source and vice versa. Figure (IV.9) illustrates the impact of the quality of the food on the numbers of ants which recruit to transfer the food to the nest. The experiment performed in VALc with 12 duplications presents the average number of workers in each box. We note that the food 1 with high quality attracts more number of ants in the first interval of the simulations. In general, the results showed that the food quality has a positive impact on the ants’ decision to follow the trail with rich food quality. Clearly, the consumption time is inversely proportional to the food quality.
Food Quality VS. Number of Ants Which Carry Food
35
^rsjrn^i^^i^oocno^—irNjro'^rLn^r^.cocno^—irsjro'^run^r^cocno^HrNjrn'^ri-n^r^. H(Nm^mLDhoooHrMm^Ln0hooaiH(Nro^Ln^Nooaiorsiro^LhLDhooai
HHHHHHHHHHrMINrMINrMrMINrMrMmmrOmrOmmrOm
Av number of ants which carry food in the box 1 number of ants which carry food in the box 2
Figure IV.9 Quality of the food vs. number of ant in each box in VALc.
36


CHAPTER V
CONCLUSION
V.1 The Model and Results
Representation is the key to the realization of any phenomenon. In this thesis, a simulation and prediction interaction model was created that is able to simulate the complex system of an ant colony by utilizing an agent based system. The proposed model has two main modes of simulation: closed and open environments that allow for observations and eventual analyses of the collective ants’ behaviors. Indirect communication presents an impressive example of cooperation, since it redirects agents to their goals in the least possible time. Pheromones represent the easiest way to communicate when the worker ant is looking for a profitable food source. Trails are affected by several factors such as evaporation rate, type of food and the distance between the food source and the nest. In this model, the user could explore the effect of many inputs on the ants’ environment. In this thesis, it was decided to investigate the role of the food’s distance from the nest and the quality type. From the resulting analysis, the distance of the food/prey from the nest was explored and had the most significant effect on the number of the ants that are recruited to carry food as well as the time for food consumption. As expected, reducing the distance between the food and the nest decreased the time within which the foragers need to carry the food to the nest and increased the number of workers which are recruited to transfer food to their home. Additional work will include simulating more behaviors of the ants and incorporating more features/parameters to the ground ants’ environment settings.
37


V.2 Further Research
This research studied the effectiveness of the pheromone on the ants’ behavior and design making. We constructed an interactive simulation model, where the users could choose their input and obtain their result in a realistic virtual environment. The ants’ (nearly) optimal solutions were obtained by selecting the shortest paths to the food source with high quality. These paths were determined via the pheromone trails. We could use the same strategy to solve or improve solution of other complex problems such as travelling salesman problem, urban traffic problem, moving packets in the network, robotics, virus propagation in cyber-attacks, and other swarm systems.
Traveling Salesman Problem is an NP-hard problem in combinatorial optimization which asks about finding the shortest possible path that passes each city exactly once and goes back to the origin city. The way how ant colony optimization works are similar to travelling salesman problem (TSP). Also, it is possible to improve solutions for a related city traffic problem by presenting model which uses the same VAL strategy, but the agents follow the negative feedback to avoid traffic. VAL provides us with both flexibility and robustness by employing different behavior tools to detect (nearly) optimal solutions and by meeting a given information computational challenge.
38


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APPENDIX
Appendix A. VALc Code
patches-own [ chemical food
box? ;;; check if this is the box boundary nest?
box-belong ;;; box boudnary belong to 0, 1, 2,3 boxes nest-scent
scent-aux ;;an auxiliary variable to helpe diffuse scent or chemical inside each box
food-source-number
]
turtles-own[out-sourcing? back-nest? new-location wiggle?]
breed[nodes node] ;;nodes of center boundary to connect tubes breed[bugs bug] ;;ants globals [
;;;;; to define the globals parameter
nest-x nest-y ;;;;;;; to determine the location of center of nest nodel node2 node3 node4 node5 node6 ;;three nodes on nest box, and others each of one connecting tubes
boxO box1 box2 box3 ;;store the box boundaries
boxOnodes box1 nodes box2nodes box3nodes ;;each of the four nodes on each boxes
boxOpatches box1 patches box2patches box3patches ;;the patches inside each box
time-before-food ;;report the time before first ant reaches food time
x-food1 ;; location of first food source
42


y-food1
x-food2
y-food2
x-food3
y-food3
]
location of first food source location of second food source location of second food source location of third food source location of third food source
555555555555555555555555555
Setup procedures
555555555555555555555555555
to setup ;;;; to setup environment clear-all ;;;;; to clean the scream
set-default-shape nodes "circle" ;;;;;; to make the shape of the node which connect tube with boxes like cicle.
set-default-shape bugs "bug"choosing the shape of agent, create-bugs population;;;;;. Take population from the input.;;
[set size 1.5 ;;;;;;. Determine agent size,
set color red ;;;;; . Determine agent color set out-sourcing? false ;;ants not get into tubes to find food set back-nest? false ;;ants not get into tubes to back to nest set wiggle? true] ;;;;;. red = not carrying food ask patch nest-x nest-y [;;;;;. sprout 1 [ ;;;;;;. Build sprout
set color orange ;;;;;. Determine sprout color set shape "circle"Determine sprout shape set size 6 ;;;;;. Determine sprout size
stamp ;;;;;
;;;;;. Determine sprout die die
]]
;;;;;;;food1 -position
5
setx-food1 0
43


set y-food 1 food 1 -position
;;;;;;;food2-position
set x-food2 food2-position * -1 set y-food 2 0 ;;;;;;;food3-position if Add-food-source[ set x-food3 food3-position sety-food3 0] setup-patches
draw-box ;;draw box for three food sources and one nest
setup-nodes
setup-tubes
reset-ticks
reset-timer
set time-before-food 0 ;;
set time 0 ;;; end ;;;; end.
to setup- environment;;;;;;;;, to setup-patches ;;;;;. Build environment, ask patches ;;;; .determine characteristic of the unit [ set box? False ;;;;;. Build boxes setup-nest ;;;;;. Build nest setup-food ;;;;;. Build food recolor-patch ;;;; .change the color
]
end ;;;;; end procedure
to setup-nodes ;generate nodes for each of the box ask box1 with[pxcor = round (x-food1 ) or pycor = round (y-food 1 ^[sprout-nodes 1[set color red]]
set box1 nodes nodes with [[box-belong] of patch-here = 1]
44


ask box2 with[pxcor = round (x-food2 ) or pycor = round (y-food2 ^[sprout-nodes 1[set color red]]
set box2nodes nodes with [[box-belong] of patch-here = 2] if Add-food-source [
ask box3 with[pxcor = round (x-food3 ) or pycor = round (y-food3 ^[sprout-nodes 1[set color red]]
set box3nodes nodes with [[box-belong] of patch-here = 3]] ask boxO with[pxcor = round (nest-x) or pycor = round (nest-y )][sprout-nodes 1 [set color red]]
set boxOnodes nodes with [[box-belong] of patch-here = 10] end
to setup-tubes ;;connect nodes use tube links ask patch nest-x nest-y[set nodel min-one-of box1 nodes [distance myself]] ask nodel [let nodex min-one-of boxOnodes[distance myself] create-link-with
nodex]
ask patch nest-x nest-y[set node2 min-one-of box2nodes [distance myself]] ask node2[let nodex min-one-of boxOnodes[distance myself] create-link-with
nodex]
if Add-food-source [
ask patch nest-x nest-y[set node3 min-one-of box3nodes [distance myself]] ask node3[let nodex min-one-of boxOnodes[distance myself] create-link-with
nodex]]
ask links [set thickness tube-width] end end procedure to setup-nest
55555555555555
5555555555'
5 represent the nest size ;;;;;; set nest? (distancexy 0 0) < 2 ;; this one just for control the shape around the nest end
to draw-box ;;drawboxes let boxO-size 6
45


let box1-size 6 let box2-size 6 let box3-size 6
if nest-box-size = "Small"[set boxO-size 6] if nest-box-size = "Medium"[set boxO-size 9] if nest-box-size = "Large"[set boxO-size 12] if food-source1-box-size = "Small"[set box1-size 6] if food-source1-box-size = "Medium"[set box1-size 9] if food-source1-box-size = "Large"[set box1-size 12] if food-source2-box-size = "Small"[set box2-size 6] if food-source2-box-size = "Medium"[set box2-size 9] if food-source2-box-size = "Large"[set box2-size 12] if Add-food-source [
if food-source3-box-size = "Small"[set box3-size 6] if food-source3-box-size = "Medium"[set box3-size 9] if food-source3-box-size = "Large"[set box3-size 12]
]
set boxO patches with[ (ceiling (abs (pxcor - nest-x )) = boxO-size and pycor >= nest-y - boxO-size and pycor <= nest-y + boxO-size)
or (ceiling(abs (pycor - nest-y ))= boxO-size and pxcor >= nest-x - boxO-size and pxcor <= nest-x + boxO-size)]
ask boxO[set pcolor white set box? true set box-belong 10] set boxOpatches patches with [ pxcor <= nest-x + boxO-size and pxcor >= nest-x - boxO-size
and pycor <= nest-y + boxO-size and pycor >= nest-y - boxO-size] ask boxOpatches[set nest-scent 200 - distancexy 0 0 set box-belong 10]; this step to draw the pheromone in the nest box
set box1 patches with[ (ceiling (abs (pxcor - x-food1 )) = box1-size and pycor >= y-food1 - box1-size and pycor <= y-food1 + box1-size)
or (ceiling(abs (pycor - y-food1 ))= box1-size and pxcor >= x-food1 - box1-size and pxcor <= x-food1 + box1-size)]
46


ask box1[set pcolor white set box? true set box-belong 1] set box1 patches patches with [ pxcor <= x-food1 + box1-size and pxcor>= x-food1 - box1-size
and pycor <= y-food1 + box1-size and pycor >= y-food1 -box1-size] ask box1 patches[set box-belong 1 let q1 food 1-quality * 100
if food-source-number = 1 [ set chemical chemical + q1]] set box2 patches with[ (ceiling (abs (pxcor - x-food2)) = box2-size and pycor >= y-food2 - box2-size and pycor <= y-food2 + box2-size)
or (ceiling(abs (pycor - y-food2 ))= box2-size and pxcor >= x-food2 - box2-size and pxcor <= x-food2 + box2-size)]
ask box2[set pcolor white set box? true set box-belong 2] set box2patches patches with [ pxcor <= x-food2 + box2-size and pxcor >= x-food2 - box2-size
and pycor <= y-food2 + box2-size and pycor >= y-food2 - box2-size] ask box2patches[set box-belong 2 let q2 food2-quality * 100
if food-source-number = 2 [ set chemical chemical + q2]] Increase the
quality
if Add-food-source If we choose 3 food sources rather than 1
set box3 patches with[ (ceiling (abs (pxcor - x-food3 )) = box3-size and pycor >= y-food3 - box3-size and pycor <= y-food3 + box3-size)
or (ceiling(abs (pycor - y-food3 ))= box3-size and pxcor >= x-food3 - box3-size and pxcor <= x-food3 + box3-size)]
ask box3[set pcolor white set box? true set box-belong 3] set box3patches patches with [ pxcor <= x-food3 + box3-size and pxcor >= x-food3 - box3-size
and pycor <= y-food3 + box3-size and pycor >= y-food3 - box3-size] ask box3patches[set box-belong 3 let q3 food3-quality * 100
if food-source-number = 3 [ set chemical chemical + q3 ]]
47


]
end ;;;;; . End previous procedure to setup-food To play with food.
if (distancexy 0 (y-food1 )) < foodl-quantity /100 Introduce food-1 on the top of the screen.
[ set food-source-number 1 ]
if (distancexy (x-food2 ) 0 ) < food2-quantity /100 Introduce food-2 on the left of the screen.
[ set food-source-number 2 ]
if Add-food-source Introduce food-3 on the right of the screen.
[
; ifelse food3-position = "close" if (distancexy (x-food3 ) 0 ) < food3-quantity /100 [ set food-source-number 3 ]
]End with adding food
if food-source-number > 0 To check food source [ set food one-of [12]];;;;. done end ;;;;;. End the procedure
to recolor-patch ;;;;. The procedure for coloring ifnotbox?[ ;;;;;. ifelse nest? ;;;;;.
[ set pcolor orange ];;;;;. Changing the color to be orange [ ifelse food > 0 ;;;;.
[ let f1 food 1-quality * 10 / 2 + 103
if food-source-number = 1 [ set pcolor f1];;;;. Add the quality to the food let f2 food2-quality * 10 / 2 + 53
if food-source-number = 2 [ set pcolor f2 ];;;;. Add the quality to the food
48


if Add-food-source [
let f3 food3-quality * 10 / 2 + 123
if food-source-number = 3 [ set pcolorf3 ]]]Add the quality to the food [ set pcolor scale-color green chemical 0.1 5 ] ]] end End the procdure to go ;; Now we produce go bottom action;;;, ask bugs ;;;. Give the instruction for the bugs [
ifelse out-sourcing? or back-nest? ;;if ant went out for food or back to nest through tube
[go-through-tube]
[ifelse color = red and sum [food] of patches > 0 ;;if not carrying food [look-for-food ;;;. Give the instruction for the bugs to search.
]
[return-to-nest];.
if wiggle?[wiggle];; if not carry food random walk go-forward-through-tube ;;find tube and outsource food go-backward-through-tube ;;carrying food and trying to back to nest ]]
if max [chemical] of patches > 0[ diffuse-scent] ;;diffuse scent ask patches
[ if box? [set chemical 0] ;;make sure no scent on box. set chemical chemical * (100 - evaporation-rate) /100 ;. Produce evaporation
equation
recolor-patch ;.
]
if sum [food] of patches > 0 and not any? bugs with [color = yellow ][set time-before-food timer ]
set time timer
49


;;;;;;;;; stop the simulation ;;;;;;;;;;;;;; if sum [food] of patches <= 0 [if not any? turtles with [color = yellow + 1]
[if (count bugs with [back-nest? or out-sourcing?] <= 0) [stop] ]];;;.
;;;;;;;;; stop the simulation ;;;;;;;;;;;;;; tick end
to diffuse-scent ;;both chemical and nest scent will diffuse ask boxOpatches[set scent-aux chemical] ;;put the chemical in a new layer diffuse scent-aux (diffusion-rate /100) ;;diffuse on the new layer ask boxOpatches [set chemical scent-aux set scent-aux 0] ;;put back only inside the box]
ask boxOpatches[set scent-aux nest-scent ]
diffuse scent-aux (diffusion-rate /100)
ask box0patches[set nest-scent scent-aux set scent-aux 0]
ask box1 patches[set scent-aux chemical]
diffuse scent-aux (diffusion-rate /100)
ask box1 patches [set chemical scent-aux set scent-aux 0]
ask box1 patches[set scent-aux nest-scent]
diffuse scent-aux (diffusion-rate /100)
ask box1 patches[set nest-scent scent-aux set scent-aux 0]
ask box2patches[set scent-aux chemical] diffuse scent-aux (diffusion-rate /100) ask box2patches [set chemical scent-aux set scent-aux 0] ask box2patches[set scent-aux nest-scent] diffuse scent-aux (diffusion-rate /100) ask box2patches[set nest-scent scent-aux set scent-aux 0] if Add-food-source [
ask box3 patches [set scent-aux chemical]
50


diffuse scent-aux (diffusion-rate /100)
ask box3patches [set chemical scent-aux set scent-aux 0]
ask box3 patches[ set scent-aux nest-scent]
diffuse scent-aux (diffusion-rate /100)
ask box3patches[set nest-scent scent-aux set scent-aux 0]]
; ask patches with [box?][set chemical 0 set nest-scent 0] end
to go-through-tube ;;go through link tube set wiggle? false forward 1
if any? nodes-on patch-ahead 1[ ask patch-ahead 1[set nest-scent nest-scent + 60] fd 3
set out-sourcing? false set back-nest? false set wiggle? true ]
; if [box?] of patch-ahead 1 [fd 2 set out-sourcing? false] end
to go-forward-through-tube "find the tube and getinto it to go out find food ifelse ([box?] of patch-ahead 1) = true[rt 180][fd 1] ;; if front is box turn around if any? boxOnodes in-radius 2[
let location one-of boxOnodes in-radius 2 ;;check if there is tube around if any?[link-neighbors] of location[
let bx [box-belong] of one-of [link-neighbors] of location ;;select the
nearby tube
if sum [food] of patches with [box-belong = bx] > 0[ if count bugs-on location < 2 [
set new-location one-of [link-neighbors] of location ;;get the possible moving direction, the other end of the tube
51


let hd floor ((towards new-location) /10) ;;set the direction if (count bugs with [out-sourcing? = true and floor (heading /10) = hd ] <= 30 and color = red)[
move-to location ;;move to the tube entrance
set heading towards new-location ;;toward to the other end of the tube if sum [food] of patches = 0 and out-sourcing? [rt 180 set out-sourcing?
false]
set out-sourcing? true ;;enter tube find food fd 1
set wiggle? false
]]]]]
end
to go-backward-through-tube ;; find the back tube and get into it to back to nest if any? nodes with [[box-belong] of patch-here != 0 and [box-belong] of patch-here != 10] in-radius 2[
let location one-of nodes with [[box-belong] of patch-here != 0 and [box-belong] of patch-here != 10] in-radius 2
if any?[link-neighbors] of location[ set new-location one-of [link-neighbors] of location let hd ((towards new-location) /10)
if (count bugs with [back-nest? or out-sourcing? and floor (heading /10) = hd] <= 30 )[
move-to location
ask location[set nest-scent nest-scent + 60] set back-nest? true set wiggle? false
set heading towards new-location
]]
]
end End the procedure.
52


to return-to-nest;;;;. To redirect the bugs to their home, ifelse nest?;;. If the patch not the nest leave the pheromone.
[
set color red ;;. After transfer food will recharge the color.
set nest-scent nest-scent + 60
rt 180
stop]
[; set nest-scent nest-scent + 60 ;; drop some chemical ;;;;;; 60 represent how much of the pheromone. That means if it is 10 then it will bw little amount and small trail;;;.
uphill-nest-scent;;. Redirect the ants to the nest, if color = yellow + 1
[set chemical chemical+60];;;;. Increase the pheromone.
]
end ;;;. End the procedure.
to look-for-food;;;. ant pick up food, else go to the strongest scent direction ifelse food > 0
;;; the color is yellow +1, not yellow [ set color yellow +1;;. Carry prey, ask patch-here[set food food -1;. Decrease the source by 1 set chemical chemical + 60 ;;leave food scent let q1 food 1-quality * 100
if food-source-number = 1 [ set chemical chemical + q1] let q2 food2-quality * 100;;. if food-source-number=2;;;.
[ set chemical chemical + q2];;;. if Add-food-source [;;. let q3 food3-quality * 100
if food-source-number = 3 [ set chemical chemical + q3 ]]
53


]
rt 180;;. After Bick up the food turn their angle, stop];;;.
[if (chemical >= 0.05) and (chemical < 2)
[ uphill-chemical]] end
;; sniff left and right, and go where the strongest smell is to uphill-chemical ;; turtle procedure let scent-ahead chemical-scent-at-angle 0 let scent-right chemical-scent-at-angle 45 let scent-left chemical-scent-at-angle -45 if (scent-right > scent-ahead) or (scent-left > scent-ahead) [ ifelse scent-right > scent-left [ rt 45 ]
[It 45]] end
;; sniff left and right, and go where the strongest smell is to uphill-nest-scent ;; turtle procedure let scent-ahead nest-scent-at-angle 0 let scent-right nest-scent-at-angle 45 let scent-left nest-scent-at-angle -45 if (scent-right > scent-ahead) or (scent-left > scent-ahead) [ ifelse scent-right > scent-left [ rt 45 ]
[It 45]] end
to wiggle ;; turtle procedure ;;;;;;; wiggle is the randome search;;;;;;;;
;;;;; move randomly 40 degree left and 40 degree right;;;;; rt random 40
54


It random 40 end
to-report nest-scent-at-angle [angle] let p patch-right-and-ahead angle 1 if p = nobody [ report 0 ] report [nest-scent] of p end
to-report chemical-scent-at-angle [angle] let p patch-right-and-ahead angle 1 if p = nobody [ report 0 ] report [chemical] of p end
;Copyright1997UriWilensky [18,19].
55


Appendix B. VALo Code
patches-own [To define the parameter which related to the chemical
food To define the food sources, nest?; To define the nest of the colony, nest-scent; to recognize the way to nest, food-source-number; define three food(prey). barriers-ant;; to identify barriers;;;;;;;;;;;;;;;;;;;.
]
;breed [barriers barrier] breed[shs sh] breed[sh2s sh] breed[sh3s sh] breed[bugs bug]
to setup; to build the environment, clear-all; clean the screen before every thing, set-default-shape shs "turtle"; to define barriers as turtle shape, set-default-shape sh2s "tree"; to define barriers as tree shape, set-default-shape sh3s "leaf; to define barriers as leaf shape, set-default-shape bugs "bug"; to define the shape of the agents, create-bugs population; Take population from the input.
[ set size 1.5; Determine agent size, set color red]; Determine agent color, ask bugs with [who = 1]
[ set color blue set size 2]
;;;; create shape for barries create-shs 1 [ set xcor10
set ycor -0.4 * max-pycor -10
56


set size 10 set color green ] create-sh2s 1 [ set xcor -35
set ycor 0.4 * max-pycor +10 set size 18 set color green ] create-sh3s 1 [ set xcor -20
set ycor -0.4 * max-pycor - 2
set size 18
set color green ]
setup-patches; to recall the procedure which great patch, reset-ticks; each tick=0.05 second, end; end the procedure.
to setup-patches; the main procedure to great the patch, ask patches; in this step, we determine the characteristic of the patch.
[ setup-nest; to great nest, setup-food; to great prey, setup-barriers-antto great barriers recolor-patch ]; determine color, end; end the procedure.
to setup-nest; the main procedure to great the nest, set nest? (distancexy 0 0) < 5; determine the location of the nest, set nest-scent 200 - distancexy 0 0; distribute sign to refer to the nest location, end; end the procedure, to setup-barriers-ant
if (distancexy 10 (-0.4 * max-pycor -10 )) < 4 [ set barriers-ant 1 ]
if (distancexy -35 (0.4 * max-pycor + 12)) < 6 [ set barriers-ant 2 ]
57


if (distancexy -20 (-0.4 * max-pycor - 2 )) < 6 [ set barriers-ant 3 ] if food-source-number = 1 [set chemical chemical + (food 1-quality * 100)] if food-source-number = 2 [set chemical chemical + (food2-quality * 100)] if food-source-number = 3 [set chemical chemical + (food3-quality * 100)] end ; end the procedure.
to setup-food; ; the main procedure to great the food.
;Food-one on the above in different paths.
;;;;;;;;;; food source 1;;;;;;;
if (distancexy 0 (food 1-position )) < foodl-quantity /100 [ set food-source-number 1 ]
!!!!!!!!!! fOOd SOUTCe 2,,,,,,,
if (distancexy (-1 * food2-position) 0 ) < food2-quantity /100 [ set food-source-number 2 ] if food-sourses-numbe = 3 [
!!!!!!!!!! fOOd SOUTCe 3,,,,,,,
if (distancexy (food3-position ) 0 ) < food3-quantity /100 [ set food-source-number 3 ] ]; setup the prey-3, if food-source-number > 0; to cheek the food.
[ set food one-of [1 2] ]; rest the food, end ; end the procedure.
to recolor-patch; the main procedure to recolor the food and the home . ifelse nest? ; to check if the nest or not.
[ set pcolor violet ]; set the color to the violet.
[ ifelse food > 0; to check the quantity of the food.
[ let f1 food 1-quality * 10 / 2 + 103 if food-source-number = 1 [ set pcolor f1]
58


let f2 food2-quality * 10 / 2 + 53 if food-source-number = 2 [ set pcolor f2 ] let f3 food3-quality * 10 / 2 + 123
if food-source-number = 3 [ set pcolor f3 ]; to check if we have another food.
]
[ set pcolor scale-color green chemical 0.1 5 ] ]; determine the pheromone color to be mix of the green and others to refer to the amount of the trail .
if barriers-ant = 1 [ set pcolor yellow ]; determine the color of barriers-1. if barriers-ant = 2 [ set pcolor yellow]; determine the color of barriers-2. if barriers-ant = 3 [ set pcolor yellow ]; determine the color of barriers-3.
End ; end the procedure.
to go; the main procedure to define go bottom, ask bugs; give the ant the instructions.
[ if who >= ticks [ stop ], if the agent number more than tick stop, ifelse (color = red) or (color = blue); check the ant who search for the food.
[ look-for-food ]; search for the prey.
[ return-to-nest ]; if you find the prey, go to the home, ifelse pcolor != yellow;. No think prevent you [wiggle;.
fd 1]; walk forward one step.
[rt 90; if you can not forward then change direction 90 degree, fd 1]; walk forward one step.
]
diffuse chemical (diffusion-rate /100); present the diffuse equation, ask patches; give the instruction to increase the pheromone.
[ set chemical chemical *(100-evaporation-rate)/100; present the evaporation equation.
recolor-patch ]; change the color.
59


55555555555555
;;;;;;;;; stop the simulation if sum [food] of patches <= 0 [if not any? bugs with [color = yellow ]
[if sum [chemical] of patches <= (100 - evaporation-rate) /100 [stop] ]] tick
end ; end the procedure.
to return-to-nest; the main procedure to redirect the ants to their home, ifelse nest?; if the patch not the nest?.
[
if color = yellow ; if the ant carry prey [set color red]
ask bugs with[who = 1] [set color blue] rt 180]
[ if pcolor != yellow
[set chemical chemical+60];increase the pheromone by 60. uphill-nest-scent ]; sniff to know the direction to the home.
End ; end the procedure.
to look-for-food; the main procedure to search for the food, if food > 0; check if we have more food in the area.
[ set color yellow; after ant carry part of food change its color, set food food - 1; decrease the eating by 1. if food-source-number = 1 [set chemical chemical + (food 1-quality * 100)] if food-source-number = 2 [set chemical chemical + (food2-quality * 100)] if food-source-number = 3 [set chemical chemical + (food3-quality * 100)];. rt 180; change the direction by 180 degree, stop ]; just stop.
if (chemical >= 0.05) and (chemical < 2); smell around and go where patch
60


has more pheromone.
[ uphill-chemical]; choosing the direction by using phermone . end ; end the procedure.
to uphill-chemical; the main procedure to choose the direction by using pheromone concentration.
let scent-aheadchemical-scent-at-angle 0; sniff the pheromone in the angle 0. let scent-rightchemical-scent-at-angle 45; sniff the pheromone in the angle 45. let scent-leftchemical-scent-at-angle-45; sniff the pheromone in the angle -45. if(scent-right>scent-ahead)or(scent-left>scent-ahead); compare the result of the pheromone concentration in these three directions.
[ifelse scent-right>scent-left; choosing the direction depend on the previous comparison.
[rt 45]; choosing the angle 45 right.
[ It 45 ] ]; choosing the angle -45 left, end ; end the procedure.
to uphill-nest-scent; the main procedure to redirect the ant to the its home, let scent-aheadnest-scent-at-angleO; smell in the angle 0. let scent-rightnest-scent-at-angle45; smell in the angle 45. let scent-leftnest-scent-at-angle-45; smell in the angle -45. if(scent-right>scent-ahead)or(scent-left>scent-ahead); compare the result of the trail concentration of the nest in these three directions.
[ifelse scent-right>scent-left; choosing the direction depend on the previous comparison.
[rt 45]; choosing the angle 45 right.
[It 45]]; choosing the angle -45 left, end ; end the procedure.
to wiggle; the main procedure for the ant to choose random angle, rt random 40; choosing the random angle from the 0 to 40 on the right side.
It random 40; choosing the random angle from the 0 to 40 on the left side.
61


if not can-move?1[rt180]; if the ant stack for some reason, end; end the procedure
to-report nest-scent-at-angle [angle]; procedure to guide the ant in patch p to its nest.
letp patch-right-and-aheadangle1;. ifp=nobody[reportO];report no ant in patch p. report[nest-scent]ofp; report sense to guide the ant to the home, end; end the procedure
to-report chemical-scent-at-angle [angle]; procedure to guide the ant in patch p to high concentration of the pheromone.
Letppatch-right-and-aheadangle1;. ifp=nobody[ report 0 ];. report no ant in patch p. report[chemical]ofp; report pheromone ratio to guide the ant to the food, end; end the procedure.
;Copyright1997UriWilensky [18,19].
62


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