An improved vector particle swarm optimization for constrained optimization problems
Information Sciences: an International Journal
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Premature convergence is the fatal shortcoming of traditional evolutionary programming. In this paper, based on the analysis of traditional evolutionary programming premature convergence, an improved multi-subgroup evolutionary programming(MEP) algorithm is proposed. In this algorithm, evolution of many subgroups is paralleled performed with different mutation strategy, and then the groups can explore the solution space separately and search the local part detailedly all together. Information is exchanged when subgroups are reorganized. Simulations based on benchmarks confirm that MEP algorithm is better than classic evolutionary programming algorithm in the aspects of global optimization, convergence speed and the robustness.