A novel multi-population cultural algorithm adopting knowledge migration

Authors:
Yi-nan Guo;Jian Cheng;Yuan-yuan Cao;Yong Lin
Affiliations:
China University of Mining and Technology, College of Information and Electronic Engineering, 221008, Xuzhou, Jiangsu, China;China University of Mining and Technology, College of Information and Electronic Engineering, 221008, Xuzhou, Jiangsu, China;China University of Mining and Technology, College of Information and Electronic Engineering, 221008, Xuzhou, Jiangsu, China;China University of Mining and Technology, College of Information and Electronic Engineering, 221008, Xuzhou, Jiangsu, China
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Recent progress in natural computation and knowledge discovery
Year:
2011

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Abstract

In existing multi-population cultural algorithms, information is exchanged among sub-populations by individuals. However, migrated individuals cannot reflect enough evolutionary information, which limits the evolution performance. In order to enhance the migration efficiency, a novel multi-population cultural algorithm adopting knowledge migration is proposed. Implicit knowledge extracted from the evolution process of each sub-population directly reflects the information about dominant search space. By migrating knowledge among sub-populations at the constant intervals, the algorithm realizes more effective interaction with less communication cost. Taken benchmark functions with high-dimension as the examples, simulation results indicate that the algorithm can effectively improve the speed of convergence and overcome premature convergence.