A Comprehensive Competitive Swarm Optimizer for Large-Scale Multiobjective Optimization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
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Original languageEnglish
Number of pages14
Journal / PublicationIEEE Transactions on Systems, Man, and Cybernetics: Systems
Online published13 Dec 2021
Publication statusOnline published - 13 Dec 2021


Competitive swarm optimizers (CSOs) have shown very promising search efficiency in large-scale decision space. However, they face difficulties when solving large-scale multi-/many-objective optimization problems (LMOPs), as their winner particles are selected by random pairwise competition based on only a single evaluation criterion, which does not provide diverse guidance for LMOPs. To alleviate this issue, this article proposes a comprehensive competitive learning (CCL) strategy for CSOs using three competition mechanisms to guide the particle search. Specifically, environmental competition classifies winner and loser particles from the swarm, while cognitive competition and social competition select one winner particle as the cognitive component and the social component, respectively, to guide the search for loser particles. This competitive learning strategy aims to enhance the search capability of loser particles and provides diverse search directions for solving LMOPs. When compared with eight competitive optimizers, the experimental results validate the high efficiency and effectiveness of our method in solving nine LMOPs with 2–10 objectives and 100–5000 variables.

Research Area(s)

  • Competitive swarm optimizer (CSO), Computer science, Convergence, Dimensionality reduction, large-scale optimization, Linear programming, multiobjective optimization, Optimization, Search problems, Space exploration