Multiparty Multiobjective Optimization By MOEA/D

Yatong Chang, Wenjian Luo, Xin Lin, Zeneng She, Yuhui Shi

Abstract: As a special class of multiobjective optimization problems (MOPs), multiparty multiobjective optimization prob-lems (MPMOPs) widely exist in real-world applications. In MPMOPs, there are multiple decision makers (DMs) concerning multiple different conflicting objectives. The goal of solving MPMOPs is to catch the best solutions satisfying all DMs as far as possible. To our best knowledge, there is little attention on solving MPMOPs, and only two optimization algorithms, i.e., OptMPNDS and OptMPNDS2, are proposed. These two algorithms are both based on non-dominated sorting genetic algorithm II (NSGA-II). However, there is no algorithm pro-posed from the decomposition perspective to solve MPMOPs. Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a popular multiobjective evolutionary optimization algorithm for MOPs. In this paper, we embed the party-by-party strategy into MOEA/D and propose the novel optimization algorithm MOEA/D-MP to solve MPMOPs. The experimental results on the benchmarks have demonstrated the effectiveness of MOEA/D-MP.