A Decomposition-Based Unified Evolutionary Algorithm for Many-Objective Problems Using Particle Swarm Optimization

Abstract

Evolutionary algorithms have proved to be efficient approaches in pursuing optimum solutions of multiobjective optimization problems with the number of objectives equal to or less than three. However, the searching performance degenerates in high-dimensional objective optimizations. In this paper we propose an algorithm for many-objective optimization with particle swarm optimization as the underlying metaheuristic technique. In the proposed algorithm, the objectives are decomposed and reconstructed using discrete decoupling strategy, and the subgroup procedures are integrated into unified coevolution strategy. The proposed algorithm consists of inner and outer evolutionary processes, together with adaptive factor μ, to maintain convergence and diversity. At the same time, a designed repository reconstruction strategy and improved leader selection techniques of MOPSO are introduced. The comparative experimental results prove that the proposed UMOPSO-D outperforms the other six algorithms, including four common used algorithms and two newly proposed algorithms based on decomposition, especially in high-dimensional targets.