An adaptive parameter control strategy for ant colony optimization

被引：2
|
作者：
Ling, Wei-Xin [1 ]
Luo, Huan-Ping [1 ]
机构：
[1] S China Univ Technol, Sch Math Sci, Guangzhou 510640, Peoples R China
来源：
CIS: 2007 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY, PROCEEDINGS | 2007年
关键词：
artificial fish swarm algorithm; ant colony optimization; parameters; TSP;
D O I：
10.1109/CIS.2007.156
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) has been proved to be one of the best performing algorithms for NP-hard combinational optimization problems like TSP. Many researchers have been attracted in research for ACO but fewer tuning methodologies have been done on its parameters which influence the algorithm directly. The setting of ACO's parameters is studied in this paper. The Artificial Fish Swarm Algorithm (AFSA) is introduced to solve the parameter timing problem, and an adaptive parameter setting strategy is proposed. It's proved to be effective by the experiment based on TSPLIB test.
引用
收藏
页码：142 / 146
页数：5
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