Interaction Enhanced Imperialist Competitive Algorithms

Algorithms

Jun-Lin Lin¹  Yu-Hsiang Tsai²  Chun-Ying Yu² 

[1]Department of Information Management, Yuan Ze University, 135 Yuan-Tung Road, Chungli, Taoyuan 32003, Taiwan

关键词: Imperialist Competition Algorithm;    Island Model Genetic Algorithm;    optimization;   

DOI  :  10.3390/a5040433

来源: mdpi

【 摘 要 】

Imperialist Competitive Algorithm (ICA) is a new population-based evolutionary algorithm. It divides its population of solutions into several sub-populations, and then searches for the optimal solution through two operations: assimilation and competition. The assimilation operation moves each non-best solution (called colony) in a sub-population toward the best solution (called imperialist) in the same sub-population. The competition operation removes a colony from the weakest sub-population and adds it to another sub-population. Previous work on ICA focuses mostly on improving the assimilation operation or replacing the assimilation operation with more powerful meta-heuristics, but none focuses on the improvement of the competition operation. Since the competition operation simply moves a colony (i.e., an inferior solution) from one sub-population to another sub-population, it incurs weak interaction among these sub-populations. This work proposes Interaction Enhanced ICA that strengthens the interaction among the imperialists of all sub-populations. The performance of Interaction Enhanced ICA is validated on a set of benchmark functions for global optimization. The results indicate that the performance of Interaction Enhanced ICA is superior to that of ICA and its existing variants.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.