Ant Colony Optimization

Front Cover
MIT Press, 2004 - Mathematics - 305 pages
4 Reviews

The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses.

The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.

 

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Contents

From Real to Artificial Ants
1
12 Toward Artificial Ants
7
13 Artificial Ants and Minimum Cost Paths
9
14 Bibliographical Remarks
21
15 Things to Remember
22
16 Thought and Computer Exercises
23
The Ant Colony Optimization Metaheuristic
25
22 The ACO Metaheuristic
33
45 Bibliographical Remarks
149
46 Things to Remember
150
47 Thought and Computer Exercises
151
Ant Colony Optimization for NPHard Problems
153
52 Assignment Problems
159
53 Scheduling Problems
167
54 Subset Problems
181
55 Application of AGO to Other NPHard Problems
190

23 How Do I Apply ACO?
38
24 Other Metaheuristics
46
25 Bibliographical Remarks
60
26 Things to Remember
61
27 Thought and Computer Exercises
63
Ant Colony Optimization Algorithms for the Traveling Salesman Problem
65
32 ACO Algorithms for the TSP
67
33 Ant System and Its Direct Successors
69
34 Extensions of Ant System
76
35 Parallel Implementations
82
36 Experimental Evaluation
84
37 ACO Plus Local Search
92
38 Implementing ACO Algorithms
99
39 Bibliographical Remarks
114
310 Things to Remember
117
Ant Colony Optimization Theory
121
42 The Problem and the Algorithm
123
43 Convergence Proofs
127
44 ACO and ModelBased Search
138
56 Machine Learning Problems
204
57 Application Principles of ACO
211
58 Bibliographical Remarks
219
59 Things to Remember
220
510 Computer Exercises
221
AntNet An ACO Algorithm for Data Network Routing
223
62 The AntNet Algorithm
228
63 The Experimental Settings
238
64 Results
243
65 AntNet and Stigmergy
252
66 AntNet Monte Carlo Simulation and Reinforcement Learning
254
67 Bibliographical Remarks
257
68 Things to Remember
258
69 Computer Exercises
259
Conclusions and Prospects for the Future
261
72 Current Trends in ACO
263
73 Ant Algorithms
271
Index
301
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About the author (2004)

Marco Dorigo is a research director of the FNRS, the Belgian National Funds for Scientific Research, and co-director of IRIDIA, the artificial intelligence laboratory of the Université Libre de Bruxelles. He is the inventor of the ant colony optimization metaheuristic. His current research interests include swarm intelligence, swarm robotics, and metaheuristics for discrete optimization. He is the Editor-in-Chief of Swarm Intelligence, and an Associate Editor or member of the Editorial Boards of many journals on computational intelligence and adaptive systems. Dr. Dorigo is a Fellow of the ECCAI and of the IEEE. He was awarded the Italian Prize for Artificial Intelligence in 1996, the Marie Curie Excellence Award in 2003, the Dr. A. De Leeuw-Damry-Bourlart award in applied sciences in 2005, the Cajastur "Mamdani" International Prize for Soft Computing in 2007, and an ERC Advanced Grant in 2010.

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