Survey on Recent Research and Implementation of Ant Colony Optimization in Various Engineering Applications

被引：0
|
作者：
Mohan, Chandra B. [1 ]
Baskaran, R. [1 ]
机构：
[1] Anna Univ, Dept Comp Sci & Engn, Madras 600025, Tamil Nadu, India
来源：
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS | 2011年 / 4卷 / 04期
关键词：
Swarm Intelligence; Ant colony optimization; Soft-computing; Engineering Applications; SCHEDULING PROBLEM; ALGORITHM; KNOWLEDGE; SYSTEMS; MODELS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Ant colony optimization (ACO) takes inspiration from the foraging behaviour of real ant species. This ACO exploits a similar mechanism for solving optimization problems for the various engineering field of study. Many successful implementations using ACO are now available in many applications. This paper reviewing varies systematic approach on recent research and implementation of ACO. Finally it presents the experimental result of ACO which is applied for routing problem and compared with existing algorithms.
引用
收藏
页码：566 / 582
页数：17
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