Application of Improved Ant Colony Algorithm

被引：1
|
作者：
Hongyan Shi [1 ]
Zhaoyu Bei [1 ]
机构：
[1] Shenyang Univ Technol, Sch Informat Sci & Engn, Shenyang 110023, Peoples R China
来源：
ICNC 2008: FOURTH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 1, PROCEEDINGS | 2008年
关键词：
D O I：
10.1109/ICNC.2008.75
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A stochastic optimization algorithm is proposed by combining ant colony (ACO) algorithm with Artificial Fish-Swarm Algorithm (AFSA) for solving continuous space optimization problems. The algorithm is improved with the rapid search capability of AFSA and the good search characteristics of ACO, and the convergence speed of the presented algorithm is also improved for avoiding being trapped in local optimization. The improved algorithm has been tested for varieties of functions. And the algorithm can handle these optimization problems very well.
引用
收藏
页码：284 / 288
页数：5
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