Computational property of hybrid methods with PSO and de

Kenichi Muranaka, Eitaro Aiyoshi

Research output: Contribution to journalArticlepeer-review


In this paper, we present a new type of hybrid methods for global optimization with Particle Swarm Optimization (PSO) and Differential Evolution (DE), which have attracted interests as heuristic and global optimization methods recently. Concretely, "p-best solutions" as the targets of PSO's particles are actuated by DE's evolutional mechanism in order to promote PSO's global searching ability. The presented hybrid method works effectively because PSO acts as a local optimizer and DE plays a role as a global optimizer. To evaluate performance of the hybridization, our method is applied to some benchmarks and is compared with the separated PSO and DE. Through computer simulations, it is certified that the proposed hybrid method performs fairy better than their separated algorithm.

Original languageEnglish
Pages (from-to)1128-1135
Number of pages8
JournalIEEJ Transactions on Electronics, Information and Systems
Issue number7
Publication statusPublished - 2012


  • Differential evolution
  • Global search
  • Hybrid method
  • Meta-heuristics
  • Particle swarm optimization

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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