Solving the distribution center location problem based on multi-swarm cooperative particle swarm optimizer

Xianghua Chu, Qiang Lu, Ben Niu, Teresa Wu

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

5 Scopus citations

Abstract

The discrete location of distribution center is a NP-hard issue and has been studying for many years. Inspired by the phenomenon of symbiosis in natural ecosystems, multi-swarm cooperative particle swarm optimizer (MCPSO) is proposed to solve the location problem. In MCPSO, the whole population is divided into several sub-swarms, which keeps a well balance of the exploration and exploitation in MCPSO. By competition and collaboration of the individuals in MCPSO the optimal location solution is obtained. The experimental results demonstrated that the MCPSO achieves rapid convergence rate and better solutions.

Original languageEnglish (US)
Title of host publicationIntelligent Computing Technology - 8th International Conference, ICIC 2012, Proceedings
Pages626-633
Number of pages8
DOIs
StatePublished - Aug 28 2012
Event8th International Conference on Intelligent Computing Technology, ICIC 2012 - Huangshan, China
Duration: Jul 25 2012Jul 29 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7389 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Intelligent Computing Technology, ICIC 2012
CountryChina
CityHuangshan
Period7/25/127/29/12

Keywords

  • discrete location
  • distribution center
  • improved PSO

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Chu, X., Lu, Q., Niu, B., & Wu, T. (2012). Solving the distribution center location problem based on multi-swarm cooperative particle swarm optimizer. In Intelligent Computing Technology - 8th International Conference, ICIC 2012, Proceedings (pp. 626-633). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7389 LNCS). https://doi.org/10.1007/978-3-642-31588-6_80