A Multi-objective Biogeography-Based Optimization with Mean Value Migration Operator

Considering its successful application in solving discrete single objective problems, biogeography-based optimization (BBO) is considered as a new promising intelligent algorithm. Therefore, many studies are conducted to apply it to solve multi-objective optimization problems (MOPs). However, these improved BBOs are not always effective because of the complexity of MOPs. A multi-objective biogeography-based algorithm with mean value migration operator named MVBBO is proposed in this paper. In MVBBO, mean value theory and new boundary constraint rule are adopted to extend the range of feasible domain. Meanwhile, mutation operator and e-dominance-based archive strategy are employed to achieve better convergence and diversity. Simulation on benchmark functions shows that the proposed MVBBO’s final Pareto solution set is better than NSGA-II and other improved multi-objective BBOs in convergence and distribution of Pareto solutions.

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