Computational properties of hybrid methods with PSO and de

Kenichi Muranaka, Eitaro Aiyoshi

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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 been attracting interest as heuristic and global optimization methods. 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 demonstrated that the proposed hybrid method performs rather better than the component algorithms separately.

Original languageEnglish
Pages (from-to)58-66
Number of pages9
JournalElectronics and Communications in Japan
Issue number4
Publication statusPublished - 2014 Apr


  • differential evolution
  • global search
  • hybrid method
  • metaheuristics
  • particle swarm optimization

ASJC Scopus subject areas

  • Signal Processing
  • Physics and Astronomy(all)
  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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