Ant Colony Optimization
Table of contents
Chapter
No.
1.
2.
3.
4.
Topics
Introduction
1.1 Origin of Ant Colony Optimization
1.2 Towards Artificial Ants
1.3 ACO Metahueristic
1.4 Applying ACO to TSP
1.4.1 Detailed implementation of TSP with ACO
1.5 Ant System and Successors
1.5.1 Elitist Ant System
1.5.2 Rank Based Ant System
1.5.3 Max-Min Ant System
1.5.3 Ant Colony System
Literature survey
Further scope
References
1
Page No.
4
5-7
7-9
9-10
10-11
11-14
14
14-15
15-16
16-18
18-19
20-22
23
24
List of Abbreviations
Abbr.
Details
AS
ACO
TSP
ASrank
MMAS
ACS
Ant System
Ant Colony Optimization
Travelling Salesman Problem
Rank Based Ant System
Max-Min Ant System
Ant Colony System
2
List of Figures
Figure No.
Fig.1
Fig.2
Fig.3
Details
Double Bridge Experiment
ACO Metahueristic Procedure
Solution Construction for TSP
3
Page No.
6
10
12
CHAPTER 1
INTRODUCTION
Ants exhibit complex social behaviors that have long since attracted the attention of human beings. Probably one of the most noticeable behaviors visible to us is the formation of socalled ant streets. When we were young, several of us may have stepped on such an ant highway or may have placed some obstacle in its way just to see how the ants would react to such disturbances. We may have also wondered where these ant highways lead to or even how they are formed. This type of question may become less urgent for most of us as we grow older and go to university, studying other subjects like computer science, mathematics, and so on. However, there are a considerable number of researchers, mainly biologists, who study the behavior of ants in detail. One of the most surprising behavioral patterns exhibited by ants is the ability of certain ant species to find what computer scientists call shortest paths.
In the early 1990s, ant colony optimization (ACO) was introduced by M.Dorigo and colleagues as a novel nature-inspired metaheuristic
Chapter
No.
1.
2.
3.
4.
Topics
Introduction
1.1 Origin of Ant Colony Optimization
1.2 Towards Artificial Ants
1.3 ACO Metahueristic
1.4 Applying ACO to TSP
1.4.1 Detailed implementation of TSP with ACO
1.5 Ant System and Successors
1.5.1 Elitist Ant System
1.5.2 Rank Based Ant System
1.5.3 Max-Min Ant System
1.5.3 Ant Colony System
Literature survey
Further scope
References
1
Page No.
4
5-7
7-9
9-10
10-11
11-14
14
14-15
15-16
16-18
18-19
20-22
23
24
List of Abbreviations
Abbr.
Details
AS
ACO
TSP
ASrank
MMAS
ACS
Ant System
Ant Colony Optimization
Travelling Salesman Problem
Rank Based Ant System
Max-Min Ant System
Ant Colony System
2
List of Figures
Figure No.
Fig.1
Fig.2
Fig.3
Details
Double Bridge Experiment
ACO Metahueristic Procedure
Solution Construction for TSP
3
Page No.
6
10
12
CHAPTER 1
INTRODUCTION
Ants exhibit complex social behaviors that have long since attracted the attention of human beings. Probably one of the most noticeable behaviors visible to us is the formation of socalled ant streets. When we were young, several of us may have stepped on such an ant highway or may have placed some obstacle in its way just to see how the ants would react to such disturbances. We may have also wondered where these ant highways lead to or even how they are formed. This type of question may become less urgent for most of us as we grow older and go to university, studying other subjects like computer science, mathematics, and so on. However, there are a considerable number of researchers, mainly biologists, who study the behavior of ants in detail. One of the most surprising behavioral patterns exhibited by ants is the ability of certain ant species to find what computer scientists call shortest paths.
In the early 1990s, ant colony optimization (ACO) was introduced by M.Dorigo and colleagues as a novel nature-inspired metaheuristic