Multiparty Multiobjective Optimization By MOEA/D

Yatong Chang1; Wenjian Luo1; Xin Lin1; Zeneng She1; Yuhui Shi2

10.1109/CEC55065.2022.9870294

2022

978-1-6654-6709-4

2022 IEEE Congress on Evolutionary Computation (CEC)

01-08

18-23 July 2022

Padua, Italy

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.

Multiparty multiobjective optimization multi-objective optimization decomposition evolutionary algorithms

其他

IEEE

1.School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China
2.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, School of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China

Yatong Chang,Wenjian Luo,Xin Lin,et al. Multiparty Multiobjective Optimization By MOEA/D[C],2022:01-08.