A Comparative Study on the Ant Colony Optimization Algorithms

被引：0
|
作者：
Adubi, Stephen A. [1 ]
Misra, Sanjay [1 ]
机构：
[1] Covenant Univ, Dept Comp & Informat Sci, Ota, Nigeria
来源：
PROCEEDINGS OF THE 2014 11TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMPUTER AND COMPUTATION (ICECCO'14) | 2014年
关键词：
metaheuristic; optimization; ant colony; SYSTEM;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The ant colony optimization (ACO) algorithm is a member of the ant colony algorithms which is part of the swarm intelligence methods. It is a probabilistic technique for finding close to optimal paths through a problem space. The ant colony optimization algorithms therefore mimic the behavior of natural ants with the use of artificial ants as agents to find a reasonable solution to optimization problems by following the model of optimization used by natural ants to get to their destination in the shortest possible time. This paper presents a review and aims to show the main variants of the ant colony optimization algorithms by comparing the results of mainly four variants on some selected combinatorial optimization problems. A review of the varieties of the ACO algorithms, application of ACO algorithms and the comparative analysis of some selected variants are presented.
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页数：4
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