An Improved Hybrid Algorithm Based on Biogeography/Complex and Metropolis for Many-Objective Optimization

Chen Wang, Yi Wang, Kesheng Wang, Y. Dong, Yang Yang

    Research output: Contribution to journalArticlepeer-review


    It is extremely important to maintain balance between convergence and diversity for many-objective evolutionary algorithms. Usually, original BBO algorithm can guarantee convergence to the optimal solution given enough generations, and the Biogeography/Complex (BBO/Complex) algorithm uses within-subsystem migration and cross-subsystem migration to preserve the convergence and diversity of the population. However, as the number of objectives increases, the performance of the algorithm decreases significantly. In this paper, a novel method to solve the many-objective optimization is called Hmp/BBO (Hybrid Metropolis Biogeography/Complex Based Optimization). The new decomposition method is adopted and the PBI function is put in place to improve the performance of the solution. On the within-subsystem migration the inferior migrated islands will not be chosen unless they pass the Metropolis criterion. With this restriction, a uniform distribution Pareto set can be obtained. In addition, through the above-mentioned method, algorithm running time is kept effectively. Experimental results on benchmark functions demonstrate the superiority of the proposed algorithm in comparison with five state-of-the-art designs in terms of both solutions to convergence and diversity.
    Original languageEnglish
    Article number2462891
    Number of pages14
    JournalMathematical Problems in Engineering
    Early online date30 Mar 2017
    Publication statusPublished - 2017


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