A modified multi-objective optimization based on brain storm optimization algorithm

Abstract

In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving multi-objective optimization problems. In this paper, A new Multi-objective optimization algorithm-Modified Multiobjective Brain Storm Optimization (MMBSO) algorithm is proposed. The clustering strategy acts directly in the objective space instead of in the solution space and suggests potential Pareto-dominance areas in the next iteration. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise (DBSCAN) clustering and Differential Evolution (DE) mutations are used to improve the performance of MBSO. A group of multi-objective problems with different characteristics were tested to validate the usefulness and effectiveness of the proposed algorithm. Experimental results show that MMBSO is a very promising algorithm for solving these tested multi-objective problems.