A New Hybrid Optimization Model Based on Imperialistic Competition and Differential Evolution Meta-Heuristic and clustering Algorithms

Metaheuristic algorithms such as imperialistic competition algorithm and differential evolution are now becoming powerful methods for solving many difficult optimization problems. In this paper, we propose a new meta-heuristic method; this method is based clustering Algorithm that is implemented by the local search in each cluster and global search between the leaders of each clusters. We also intend to combine the advantages of existing algorithms into the new optimization model based clustering Algorithm. In each cluster, we use the behavior of the colony in the ICA and the DE for the leader to survey the search space. After a detailed explanation of this algorithm, we will then compare the proposed model with the other algorithms such as: particle swarm optimization, genetic, imperialistic competition and differential evolution algorithms. The results show the ability of this new algorithm in dealing with different types of optimization problems.