ANT COLONY OPTIMIZATION MARCO DORIGO AND THOMAS STTZLE PDF

Tygozil This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. Combinatorial optimization via the simulated cross-entropy method. Table of Contents Preface Acknowledgments 1.

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It gives a broad overview of many aspects of ACO, ranging from a detailed description of the ideas underlying ACO, to the definition Read more This book introduces the rapidly growing field of ant colony optimization. It gives a broad overview of many aspects of ACO, ranging from a detailed description of the ideas underlying ACO, to the definition of how ACO can generally be applied to a wide range of combinatorial optimization problems, and describes many of the available ACO algorithms and their main applications.

The book first describes the translation of observed ant behaviour into working optimization algorithms. The ant colony metaheuristics 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 network routing problem, is described in detail. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. The book is intended primarily for 1 academic and industry researchers in operations research, arti-ficial intelligence, and computational intelligences; 2 practitioners willing to learn how to implement ACO algorithms to solve combinatorial optimization problems; and 3 graduate and postgraduate students in computer science, management studies, operations research, and artificial intelligence.

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Ant Colony Optimization

It gives a broad overview of many aspects of ACO, ranging from a detailed description of the ideas underlying ACO, to the definition Read more This book introduces the rapidly growing field of ant colony optimization. It gives a broad overview of many aspects of ACO, ranging from a detailed description of the ideas underlying ACO, to the definition of how ACO can generally be applied to a wide range of combinatorial optimization problems, and describes many of the available ACO algorithms and their main applications. The book first describes the translation of observed ant behaviour into working optimization algorithms.

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ANT COLONY OPTIMIZATION MARCO DORIGO AND THOMAS STTZLE PDF

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.

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Ant Colony Optimization (English) 1st Edition (Paperback)

Overview[ edit ] In the natural world, ants of some species initially wander randomly , and upon finding food return to their colony while laying down pheromone trails. If other ants find such a path, they are likely not to keep travelling at random, but instead to follow the trail, returning and reinforcing it if they eventually find food see Ant communication. The more time it takes for an ant to travel down the path and back again, the more time the pheromones have to evaporate. A short path, by comparison, gets marched over more frequently, and thus the pheromone density becomes higher on shorter paths than longer ones. Pheromone evaporation also has the advantage of avoiding the convergence to a locally optimal solution. If there were no evaporation at all, the paths chosen by the first ants would tend to be excessively attractive to the following ones.

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Ant colony optimization algorithms

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