A Decomposition Method Based on Random Objective Division for MOEA/D in Many-Objective Optimization

Abstract

With the number increase of optimization objectives, the selection pressure begins to decrease, and the performance of multi-objective evolutionary algorithms becomes gradually inefficient. A decomposition method based on random objective division (ROD) is proposed in this paper for MOEA/D optimizing many-objective problems. We abbreviate MOEA/D using ROD decomposition as MOEA/D-ROD for easy expression. MOEA/D-ROD adopts a random objective partitioning method transforming a many-objective problem into multiple multi-objective problems. Furthermore, each multi-objective problem utilizes the assigned decomposition method to transform itself into multiple single-objective optimization problems for collaborative optimization. Therefore, different decomposition methods can be combined at the same time to balance diversity and convergence of the algorithm. Three sets of experiments are carried out on two sets of scalable problems DTLZ 1 - 4 and WFG 1 - 9, with the number of objectives from 3-8, 10 and 15. The experimental results verify the effectiveness of the proposed ROD decomposition method in solving those many-objective optimization problems.

Keywords

• Many-objective optimization
• decomposition
• random dimension division
• evolutionary algorithm