An evolutionary many-objective optimization algorithm based on population decomposition and reference distance

Abstract

An evolutionary multi-objective optimization (EMO) algorithm based on Pareto dominance have made great achievements in dealing with two or three objectives optimization problems. Nevertheless, with the problem objective function of the number increasing, the selection pressure of Pareto dominance based EMO algorithms deteriorates rapidly. In our paper, we propose an evolutionary algorithm based on M2M population decomposition and reference distance to solve many-objective optimization problems, named EAPD-RD. By population decomposition, the population is decomposed into several subpopulation, and each subpopulation corresponds to a subspace represented by a direction vector. Each individual has a projector distance along the direction vector and a reference distance to the direction vector. The proposed algorithm use projector distance to enhance selection pressure of Pareto dominance. A Niching techniques based on reference distance is designed to the maintain the population diversity. Contrast experiments have been conducted by testing DTLZ(1-4) problems with 8, 10 and 15 objectives with proposed algorithm, MOEA/D and GrEA. The results of simulation illustrate that our algorithm outperforms MOEA/D and GrEA under the IGD metric.