Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant Marco Dorigo, Thomas Stützle; Published in IEEE Computational. Results 1 – 11 of 11 Ant Colony Optimiza by Marco Dorigo & Thomas St?tzle and a great Ant Colony Optimization and Swarm Intelligence: 4th: Editor-Marco. Marco Dorigo, Thomas Stützle, Ant Colony Optimization, Bradford Company, Scituate, MA Holger Hoos, Thomas Sttzle, Stochastic Local Search: Foundations.

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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 sftzle 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 ACOthe 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 marfo, 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 – Semantic Scholar

This is essential reading not only for those working in artificial intelligence and optimization, but for all of us who find the interface between biology and technology fascinating. Ant Colony Optimization presents the most successful algortihmic techniques to be developed on the basis on ant behavior.


This book mardo certainly open the gates for new experimental thomad on decision making, division of labor, and communication; moreover, it will also inspire all those studying patterns of self-organization. Leia mais Leia menos. Detalhes do produto Capa dura: Bradford Book 4 de junho de Idioma: Seja o primeiro a avaliar este item. Vorigo 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. Compartilhe seus pensamentos com outros clientes. Being an ant isn’t very complex, but it’s a daily fight for life.

The losers in that fight don’t count, but the winners get to vote. That is the basis of ant colony optimization. There are many parts to the idea, all of them very simple. First, there are many routes to the goal food, if you’re an ant – some are better, some worse, you don’t know which are which in advance, and the answer may change over time.

Second, it’s a random search. If you find any answer at all, no matter how convoluted, you get to vote on your route. Third, there volony many other ants, all voting. Any leg of a trip that is heavily followed must be part of a good route, and gets many votes.

There are details, but that’s about it. Chapters are the most readable, and convey the basic spirit of the family of algorithms.

Ant colony optimization

Dorigo, the principal author and founder of the ant school, uses this chapter to express his pure joy at having found such a wonderful thing, and at the similar approaches that others have also found. The approach has some real limits. For example, it can solve only problems that look like finding the shortest route. The good news is that a wide droigo of unlikely problems can all be cast in these terms. The better news is that, given the many variations available, some form of the ‘stigmergic’ approach will probably solve any problem in that range.

Best of all, though, is the sheer cleverness and the sincere appreciation expressed by the authors. Nature is economical, but a brilliant problem solver.

Ant Colony Optimization – Livros na Amazon Brasil-

This is written by someone who as able to listen in on one of the lessons. Ant Colony Optimization focuses on the fact amrco ants foraging for food will quickly form a trail that is the shortest thoas ditance betwen the food and home. Rach ant follows the scent trail laid on a path by previous travelers and adds its own pheromone to the scent, both going and doriho. With a choice, ants tend to follow the strongest scent. Of a pioneer pair, the one choosing the shortest path will make the round trip before the other.

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Each pheromone trace evaporates in time, but an ant’s antenna can detetct the slightest trace. That is a simplification of the introductory chapters of the book. The “pheromone trail” scheme is used to devise “artificial ant” which then takes part in the comnstruction of powerful ant algorithms for solving intractable problems such as the classical “Traveling Salesman” and other routing problems.

The book is a complete text for a college course, with a large bibliography and many internal references to sources on the Internet. It is well written, with pseudo code showing how each algorithm can form computer programs. I can’t evaluate the difficulty, but for me the math in later chapters is above my reach, but gratifying, nevertheless.

Its a perfect exposition by the Originator himself on how events in thoas helped in the maro of one of the most powerful algorithms in vogue today Arrived on time and is sorigo as advertised.

Fifteen years after the elegant double-bridge experiments by Deneubourg et al. This book gives a well paced introduction to ACO, describes its use in various optimization problems and gives interesting examples of its applications in industry.

Explanations are clear and concise and, with the exception of a few well defined technical terms, free of scientific jargon. It is a pleasure to read for everyone with an interest in optimization theory.

However, if you are looking for a book that celebrates the beauty of nature’s problem solving capabilities, you are better of with “Swarm Intelligence” or Flake’s “Computational Beauty of Nature”. The initial idea of ACO may be bio-inspired, but this book has a crystal clear optimizatiob of the computational considerations in optimization theory.

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