1. A hybrid ant colony optimization for continuous domains
- Author
-
Xiao, Jing and Li, LiangPing
- Subjects
- *
ANT algorithms , *MATHEMATICAL optimization , *MACHINE learning , *ARTIFICIAL intelligence , *MACHINE theory , *PROBABILITY theory , *FUNCTIONAL analysis , *MATHEMATICAL analysis - Abstract
Abstract: 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. [Copyright &y& Elsevier]
- Published
- 2011
- Full Text
- View/download PDF