Ant Colony Optimization

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  • Topic: Ant colony optimization, Travelling salesman problem, Genetic algorithm
  • Pages : 29 (7210 words )
  • Download(s) : 143
  • Published : July 9, 2012
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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 for the solution of hard combinatorial optimization (CO) problems. ACO belongs to the class of metaheuristics [3], which are approximate algorithms used to obtain good enough solutions to hard CO problems in a reasonable amount of computation time. Ant Colony Optimization (ACO) is a recently proposed metaheuristic approach for solving hard combinatorial optimization problems. The inspiring source of ACO is the pheromone trail laying and following behavior of real ants, which use pheromones as a communication medium. In analogy to the biological example, ACO is based on the indirect communication of a colony of simple agents, called (artificial) ants, mediated by (artificial) pheromone trails. The pheromone trails in ACO serve as distributed, numerical information, which the ants use to probabilistically construct solutions to the problem being solved, and which the ants adapt during the algorithm’s execution to reflect their search experience.

The first example of such an algorithm is Ant System (AS), which was proposed using as an example application the well-known Traveling Salesman Problem (TSP). Despite encouraging initial results, AS could not compete with state-of-the-art algorithms for the TSP. Nevertheless, it had the important role of stimulating further research on algorithmic variants, which obtain much better computational performance, as well as on applications to a large variety of different problems. In fact, now there exists a considerable amount of 4

applications obtaining world class performance on problems like the quadratic assignment, vehicle routing, sequential ordering, scheduling, routing in Internet-like networks, and so on. The (artificial) ants in ACO implement a randomized construction heuristic, which makes probabilistic decisions as a function of artificial pheromone trails and possibly available heuristic information based on the input data of the problem to be solved. As such, ACO can be interpreted as an extension of traditional...
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