A self -organizing weighted optimization based framework for large -scale multi-objective optimization

The solving of large-scale multi-objective optimization problem (LSMOP) has become a hot research topic in evolutionary computation. To better solve this problem, this paper proposes a self-organizing weighted optimization based framework, denoted S-WOF, for addressing LSMOPs. Compared to the original framework, there are two main improvements in our work. Firstly, S-WOF simplifies the evolutionary stage into one stage, in which the evaluating numbers of weighted based optimization and normal optimization approaches are adaptively adjusted based on the current evolutionary state. Specifically, regarding the evaluating number for weighted based optimization (i.e., t(1) ), it is larger when the population is in the exploitation state, which aims to accelerate the convergence speed, while t(1) is diminishing when the population is switching to the exploration state, in which more attentions are put on the diversity maintenance. On the other hand, regarding the evaluating number for original optimization (i.e., t(2) ), which shows an opposite trend to t(1) , it is small during the exploitation stage but gradually increases later. In this way, a dynamic trade-off between convergence and diversity is achieved in SWOF. Secondly, to further improve the search ability in the large-scale decision space, an efficient competitive swarm optimizer (CSO) is implemented in S-WOF, which shows efficiency for solving LSMOPs. Finally, the experimental results have validated the superiority of S-WOF over several state-of-the-art large-scale evolutionary algorithms.

Automated summary

This paper proposes a self-organizing weighted optimization based framework (S-WOF) for solving large-scale multi-objective optimization problems (LSMOPs). The S-WOF achieves a dynamic trade-off between convergence and diversity by adjusting the weights and evaluation numbers. In addition, an efficient competitive swarm optimizer (CSO) is implemented to improve the search ability. Experimental results demonstrate the superiority of S-WOF over several state-of-the-art large-scale evolutionary algorithms.