Adaptive sharing scheme based sub-swarm multi-objective PSO

Yanxia Sun, Zenghui Wang

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


To improve the optimization performance of multi-objective particle swarm optimization, a new sub-swarm method, where the particles are divided into several sub-swarms, is proposed. To enhance the quality of the Pareto front set, a new adaptive sharing scheme, which depends on the distances from nearest neighbouring individuals, is proposed and applied. In this method, the first sub-swarms particles dynamically search their corresponding areas which are around some points of the Pareto front set in the objective space, and the chosen points of the Pareto front set are determined based on the adaptive sharing scheme. The second sub-swarm particles search the rest objective space, and they are away from the Pareto front set, which can promote the global search ability of the method. Moreover, the core points of the first sub-swarms are dynamically determined by this new adaptive sharing scheme. Some Simulations are used to test the proposed method, and the results show that the proposed method can achieve better optimization performance comparing with some existing methods.

Original languageEnglish
Pages (from-to)673-691
Number of pages19
JournalJournal of Universal Computer Science
Issue number7
Publication statusPublished - 2017


  • Adaptive sharing scheme
  • Multi-objective PSO
  • Pareto front set
  • Sub-swarm

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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