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.
|Published (Last):||23 January 2015|
|PDF File Size:||9.39 Mb|
|ePub File Size:||9.52 Mb|
|Price:||Free* [*Free Regsitration Required]|
The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization.
Arrived on time and is exactly as advertised. An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. Ant colony thlmas algorithms Mathematical optimization. With a choice, ants tend to follow the strongest scent. Ant colony optimization ACO takes inspiration from the foraging behavior of some ant species.
Third, there are many other ants, all voting. 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.
Table of Contents Preface Acknowledgments 1. This paper has highly influenced 36 other papers. Showing of extracted citations.
Chapters are the most readable, and convey the basic spirit of the family of algorithms. Leia mais Leia menos.
Ant colony optimization – Semantic Scholar
For example, it can solve only problems that look like finding the shortest route. Citation Statistics Citations 0 20 40 ’06 ’09 ’12 ’15 ‘ AntNet, an ACO algorithm designed for network routing problem, is described in detail.
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 ACOthe most successful and widely recognized algorithmic technique based on ant behavior. Nature is economical, but a brilliant problem solver. Due-date assignment and machine scheduling in a low machine-rate situation with stochastic processing times Mehdi IranpoorSeyyed M.
This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. Ant Colony Optimization presents the most successful algortihmic techniques to be developed on the basis on ant behavior. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn marcl to implement ACO algorithms.
Ant colony optimization
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.
The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. Ant Colony Optimization focuses on the fact that ants foraging for food will quickly form a trail that is the shortest possible ditance betwen the food and home.
EscarioJuan F. Bradford Book 4 de junho de Idioma: The book is a rhomas text for a college course, with a large bibliography and many internal references to sources on the Internet. The book is intended primarily for 1 academic and industry researchers in operations research, arti-ficial intelligence, and computational intelligences; 2 practitioners willing to mraco 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.
Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. References Publications referenced by this paper. Have doubts regarding this product?
This book will certainly open the gates for new experimental work on decision making, division of labor, and communication; moreover, it will also inspire all those studying patterns lptimization self-organization.
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.
Topics Discussed in This Paper. 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. 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 initial idea of ACO may be bio-inspired, but this book has a crystal clear focus of the computational considerations in optimization theory.