An Improved Hybrid Algorithm Based on BiogeographyComplex and Metropolis for Many-Objective Optimization

Abstract EN

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.