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A Hybrid Ant Colony Optimization for Continuous Domains
XIAO Jing 1 *,LI LiangPing 2
1.School of Computer Science, South China Normal University
2.Department of Computer Science, Sun Yat-sen University, Guangzhou, 510006
*Correspondence author
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Funding: Specialized Research Fund for the Doctoral Program of Higher Education （No.No. 20090171120003）
Opened online:18 July 2011
Accepted by: none
Citation: XIAO Jing,LI LiangPing.A Hybrid Ant Colony Optimization for Continuous Domains[OL]. [18 July 2011] http://en.paper.edu.cn/en_releasepaper/content/4435170
 
 
Research on optimization in continuous domains gains much of focus in swarm computation recently. A hybrid ant colony optimization approach which combines with the continuous population-based incremental learning and the differential evolution for continuous domains is proposed in this paper. It utilizes the ant population distribution and combines the continuous population-based incremental learning to dynamically generate the Gaussian probability density functions during evolution. To alleviate the less diversity problem in traditional population-based ant colony algorithms, differential evolution is employed to calculate Gaussian mean values for the next generation in the proposed method. Experimental results on a large set of test functions show that the new approach is promising and performs better than most of the state-of-the-art ACO algorithms do in continuous domains.
Keywords:Continuous optimization; Ant colony optimization; Continuous population-based incremental learning; Differential evolution.
 
 
 

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