## Ant Colony OptimizationThe 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 |

301 | |