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

Chen Wang, Yi Wang, Kesheng Wang, Yao Dong and Yang Yang

Mathematical Problems in Engineering, 2017, vol. 2017, 1-14

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:2462891

DOI: 10.1155/2017/2462891

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