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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

12 Scopus Citations
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Author(s)

  • Yongfeng Li
  • Lingjie Li
  • Qiuzhen Lin
  • Zhong Ming
  • Carlos A. Coello Coello

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number101084
Journal / PublicationSwarm and Evolutionary Computation
Volume72
Online published4 May 2022
Publication statusPublished - Jul 2022

Abstract

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., t1), it is larger when the population is in the exploitation state, which aims to accelerate the convergence speed, while t1 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., t2), which shows an opposite trend to t1, 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 S-WOF. 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.

Research Area(s)

  • Competitive swarm optimizer (CSO), Large-scale optimization, Weighted optimization

Citation Format(s)

A self-organizing weighted optimization based framework for large-scale multi-objective optimization. / Li, Yongfeng; Li, Lingjie; Lin, Qiuzhen et al.
In: Swarm and Evolutionary Computation, Vol. 72, 101084, 07.2022.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review