An Improved Weighted Optimization-based Framework for Large-scale MOPs
Junhao Zheng, Lingjie Li, Qiuzhen Lin, Zhong Ming · 2021
This paper proposes an improved weighted optimization-based framework (iWOF) for solving large-scale multiobjective optimization problems (LSMOPs). Compared to the original framework, there are two main contributions in our work. Firstly, a novel evolutionary search strategy involving two different search operators with different search characteristics, i.e., a particle swarm optimization (PSO) operator and differential evolution (DE) operator, is designed in iWOF, which aims to provide a robust search ability on finding optimal solutions in a huge decision space. Secondly, different from the stage in the original framework that divides the whole evolutionary process into two independent stages, the evolutionary process in the proposed iWOF is simplified to only one stage, which effectively reduces the number of predefined parameters. Besides that, the evolving numbers of weight optimization and original optimization in iWOF are adjusted adaptively according to the evolutionary stage. The experimental results on three different groups of benchmark LSMOPs validate the superiority of the proposed iWOF over WOF and other several state-of-the-art multiobjective evolutionary algorithms.