Theoretical And Practical Aspects Of Ant Colony Optimization
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Theoretical and Practical Aspects of Ant Colony Optimization
Combinatorial optimization problems are of high academical and practical importance. Unfortunately, many of them belong to the class of NP-hard problems and are therefore intractable. In other words, as their dimension increases, the time needed by exact methods to find an optimal solution grows exponentially. Metaheuristics are approximate methods for attacking these problems. An approximate method is a technique that is applied in order to find a good enough solution in a reasonable amount of time. Examples of metaheuristics are simulated annealing, tabu search, evolutionary computation, and ant colony optimization (ACO), the subject of this book. The contributions of this book to ACO research are twofold. First, some new theoretical results are proven that improve our understanding of how ACO works. Second, a new framework for ACO algorithms is proposed that is shown to perform at the state-of-the-art level on some important combinatorial optimization problems such as the k-cardinality tree problem and the group shop scheduling problem, which is a general shop scheduling problem that includes among others the well-known job shop scheduling and the open shop scheduling problems.
Ant Colony Optimization
An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. 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.
Ant Colony Optimization and Swarm Intelligence
1 With its fourth edition, the ANTS series of workshops has changed its name. The original"ANTS-From Ant Colonies to Artificial Ants: International Workshop on Ant Algorithms" has become "ANTS - International Workshop on Ant Colony Optimization and Swarm Intelligence". This change is mainly due to the following reasons. First, the term "ant algorithms" was slower in spreading in the research community than the term "swarm intelligence", while at the same time research inso-called swarm robotics was the subject of increasing activity: it was therefore an obvious choice to substitute the term ant algorithms with the more accepted and used term swarm intelligence. Second, although swarm intelligence research has undoubtedly produced a 2 number of interesting and promising research directions, we think it is fair to say that its most successful strand is the one known as "ant colony optimization". Ant colony optimization, first introduced in the early 1990s as a novel tool for the approximate solution of discrete optimization problems, has recently seen an explosion in the number of its applications, both to academic and real-world problems, and is currently being extended to the realm of continuous optimization (a few papers on this subject being published in these proceedings). It is therefore a reasonable choice to have the term ant colony optimization as part of the workshop name