A Bagging Based Multiobjective Differential Evolution With Multiple Subpopulations

Different from multiobjective differential evolution algorithm (MODE) based on traditional mutation operators and a single population, this paper developed a bagging based MODE with multiple subpopulations (BagMPMODE) by incorporating the idea of bagging into the evolution process of MODE. In this algorithm, multiple subpopulations with different evolution operators are adopted to maintain search diversity, as did by some previous researches on MODE. During evolution, the subpopulations will compete with each other, i.e., the size of each subpopulation will be adjusted based on its contribution to the whole search result. Based on the multiple subpopulation strategy, the idea of bagging ensemble is adopted to generate offspring solutions, which can be viewed as the cooperation of these multiple subpopulations. The proposed BagMPMODE algorithm is evaluated on a set of 22 benchmark problems, and computational experiments illustrate that the BagMPMODE algorithm is competitive or even superior to several state-of-the-art MODEs and some other multiobjective evolutionary algorithms in the literature for most problems.

Automated summary

This paper introduces a bagging based multiobjective differential evolution algorithm with multiple subpopulations, which competes and cooperates to generate offspring solutions, effectively maintaining search diversity. Experimental results on 22 benchmark problems show that the proposed algorithm outperforms several state-of-the-art MODEs and other multiobjective evolutionary algorithms in the literature.