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

    Abstract

    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
    Volume2017
    Early online date30 Mar 2017
    DOIs
    Publication statusPublished - 2017

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