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Interaction Enhanced Imperialist Competitive Algorithms
AbstractImperialist 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.
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Lin, J.-L.; Tsai, Y.-H.; Yu, C.-Y.; Li, M.-S. Interaction Enhanced Imperialist Competitive Algorithms. Algorithms 2012, 5, 433-448.View more citation formats
Lin J-L, Tsai Y-H, Yu C-Y, Li M-S. Interaction Enhanced Imperialist Competitive Algorithms. Algorithms. 2012; 5(4):433-448.Chicago/Turabian Style
Lin, Jun-Lin; Tsai, Yu-Hsiang; Yu, Chun-Ying; Li, Meng-Shiou. 2012. "Interaction Enhanced Imperialist Competitive Algorithms." Algorithms 5, no. 4: 433-448.