Ant algorithms for discrete optimization

被引：1839
|
作者：
Dorigo, M
Di Caro, G
Gambardella, LM
机构：
[1] Free Univ Brussels, IRIDIA, B-1050 Brussels, Belgium
[2] IDSIA, CH-6900 Lugano, Switzerland
来源：
ARTIFICIAL LIFE | 1999年 / 5卷 / 02期
关键词：
ant algorithms; ant colony optimization; swarm intelligence; metaheuristics; natural computation;
D O I：
10.1162/106454699568728
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.
引用
收藏
页码：137 / 172
页数：36
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