Initial Population Generation Method and its Effects on MOEA/D

Abstract

A good initial population generation method is of necessity to improve the performance of evolutionary multiobjective optimization (EMO) algorithms. However, until now only a few methods for generating an initial population have been proposed for EMO algorithms. In this paper, we propose a simple idea of generating an initial population for a popular decomposition-based algorithm, i.e., MOEA/D with the penalty-based boundary intersection (PBI) function, and demonstrate its effectiveness. The basic idea is to generate more initial solutions than the population size and to assign an appropriate solution to each weight vector. Firstly, we modify the initialization phase of MOEA/D through two different strategies based on this idea. Then, the modified MOEA/D algorithms are compared with the original MOEA/D on frequently-used many-objective test problems: DTLZ1, DTLZ3 and DTLZ4. Our experimental results clearly show that the proposed initial population generation method can significantly improve the performance of the original MOEA/D.