Multiple swarm intelligence methods based on multiple population with sharing best solution for drastic environmental change

Yuta Umenai, Hiroyuki Sato, Fumito Uwano, Keiki Takadama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes the multiple swarm optimization method composed of some numbers of populations, each of which is optimized by the different swarm optimization algorithm to adapt to dynamically change environment. To investigates the effectiveness of the proposed method, we apply it into the complex environment, where the objective function changes in a certain interval. The intensive experiments have revealed that the performance of the proposed method is better than the other conventional algorithms (i.e., particle swarm optimization (PSO), cuckoo search (CS), differential evolution (DE)) in terms of convergence and fitness.

Original languageEnglish
Title of host publicationGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages97-98
Number of pages2
ISBN (Electronic)9781450357647
DOIs
Publication statusPublished - Jul 6 2018
Externally publishedYes
Event2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan
Duration: Jul 15 2018Jul 19 2018

Publication series

NameGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion

Other

Other2018 Genetic and Evolutionary Computation Conference, GECCO 2018
CountryJapan
CityKyoto
Period7/15/187/19/18

Keywords

  • Cuckoo search
  • Differential evolution
  • Multiple population
  • Particle swarm optimization
  • Swarm optimization

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computational Theory and Mathematics
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Multiple swarm intelligence methods based on multiple population with sharing best solution for drastic environmental change'. Together they form a unique fingerprint.

Cite this