Abstract
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.
Highlights
IntroductionOptimization is a rapidly developing field due to its practical application to many real life problems
Optimization is a rapidly developing field due to its practical application to many real life problems.In this paper, we study a special class of optimization problem in the form of: min f(x), s.t
The assimilation operation of Imperialist Competitive Algorithm (ICA) often converges to a local optimum prematurely [29,30,32], and many attempts have been proposed to replace it with more powerful operations
Summary
Optimization is a rapidly developing field due to its practical application to many real life problems. We study a special class of optimization problem in the form of: min f(x), s.t. L ≤ x ≤ U (1). Where f(x) is the objective function to be minimized, x = (x1, x2, ..., xn) Rn is the variable vector, and. When the objective function f(x) is nonlinear and has many variables (i.e., n is large), problem (1). Becomes hard to solve directly by conventional mathematical methods, such as gradient descent. To resolve this difficulty, evolutionary algorithms based on meta-heuristics have been applied to solve problem (1). Some notable meta-heuristics include Genetic Algorithm [1,2], Particle Swarm
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