Cooperative coevolution for large-scale optimization based on kernel fuzzy clustering and variable trust region methods

Jianchao Fan, Jun Wang, Min Han

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

Large-scale optimization arises in a variety of scientific and engineering applications. In this paper, a particle swarm optimization (PSO) approach with dynamic neighborhood that is based on kernel fuzzy clustering and variable trust region methods (called FT-DNPSO) is proposed for large-scale optimization. The cooperative coevolution incorporated with a kernel fuzzy C-means clustering strategy is introduced to divide high-dimensional problems in to subproblems, and explore their search spaces. Furthermore, the independent variable ranges change adaptably by using the variable trust region learning method, which expedites the convergence process and explores in the effective space. In addition, the dynamic neighborhood topology assists the PSO algorithm in cooperating with neighbor particles and avoids the problem of premature convergence. Simulation results substantiate the effectiveness of the proposed algorithm to solve large-scale optimization problems with many well-known benchmark functions.

Original languageEnglish
Article number6576136
Pages (from-to)829-839
Number of pages11
JournalIEEE Transactions on Fuzzy Systems
Volume22
Issue number4
DOIs
Publication statusPublished - Aug 2014
Externally publishedYes

Keywords

  • Cooperative coevolution (CC)
  • dynamic neighborhood topology
  • kernel fuzzy clustering
  • large scale optimization
  • particle swarm optimization (PSO)
  • subswarms
  • trust region

Fingerprint

Dive into the research topics of 'Cooperative coevolution for large-scale optimization based on kernel fuzzy clustering and variable trust region methods'. Together they form a unique fingerprint.

Cite this