Ant Colony Optimization (Hardcover)

Marco Dorigo, Thomas Stützle

Ant Colony Optimization (Hardcover)
  • 出版商: MIT
  • 出版日期: 2004-06-04
  • 售價: $1,900
  • 貴賓價: 9.5 折 $1,805
  • 語言: 英文
  • 頁數: 319
  • 裝訂: Hardcover
  • ISBN: 0262042193
  • ISBN-13: 9780262042192

    • 已絕版

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Description:

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.

Marco Dorigo is research director of the IRIDIA lab at the Université Libre de Bruxelles and the inventor of the Ant Colony Optimization metaheuristic for combinatorial optimization problems.

Thomas Stützle is Assistant Professor in the Computer Science Department at Darmstadt University of Technology.

 

Table of Contents:

 

Preface ix
 
  Acknowledgments xiii
 
1 From Real to Artificial Ants 1
 
2 The Ant Colony Optimization Metaheuristic 25
 
3 Ant Colony Optimization Algorithms for the Traveling Salesman Problem                                 65
 
4 Ant Colony Optimization Theory 121
 
5 Ant Colony Optimization for NP-Hard Problems 153
 
6 AntNet: An ACO Algorithm for Data Network Routing 223
 
7 Conclusions and Prospects for the Future 261
 
  Appendix: Sources of Information about the ACO Field 275
 
  References 277
 
  Index 301

商品描述(中文翻譯)

描述：
螞蟻的複雜社會行為一直是科學研究的重點，計算機科學家現在發現這些行為模式可以為解決困難的組合優化問題提供模型。受螞蟻行為一個方面的啟發，開發能夠找到計算機科學家所稱的最短路徑的算法，已經成為螞蟻群體優化（Ant Colony Optimization, ACO）領域，這是基於螞蟻行為的最成功和廣泛認可的算法技術。本書概述了這一快速增長的領域，從其理論起源到實際應用，包括許多可用的ACO算法及其用途的描述。

本書首先描述了如何將觀察到的螞蟻行為轉化為可運作的優化算法。接著介紹了螞蟻群體元啟發式算法，並在組合優化的一般背景下進行探討。隨後詳細描述並指導所有主要的ACO算法，並報告當前的理論發現。本書調查了目前正在使用的ACO應用，包括路由、分配、排程、子集、機器學習和生物信息學問題。詳細描述了為網絡路由問題設計的ACO算法AntNet。作者總結了該領域的進展並概述了未來的研究方向。每章結尾都有參考資料、列出章節中重要觀念的要點以及練習題。《螞蟻群體優化》將吸引希望學習如何實施ACO算法的學術界和業界研究人員、研究生及實務工作者。

Marco Dorigo是布魯塞爾自由大學IRIDIA實驗室的研究主任，也是針對組合優化問題的螞蟻群體優化元啟發式算法的發明者。

Thomas Stützle是達姆施塔特工業大學計算機科學系的助理教授。

目錄：
前言
致謝
1. 從真實螞蟻到人工螞蟻
2. 螞蟻群體優化元啟發式算法

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