%0 Journal Article
%T Interaction Enhanced Imperialist Competitive Algorithms
%A Jun-Lin Lin
%A Yu-Hsiang Tsai
%A Chun-Ying Yu
%A Meng-Shiou Li
%J Algorithms
%D 2012
%I MDPI AG
%R 10.3390/a5040433
%X 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.
%K Imperialist Competition Algorithm
%K Island Model Genetic Algorithm
%K optimization
%U http://www.mdpi.com/1999-4893/5/4/433