A modified shuffled frog leaping algorithm with convergence of update process in local search

  • Qiusheng Wang*
  • , Hao Yang
  • , Xiaoyao Sun
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Shuffled frog leaping algorithm (SFLA) is meta-heuristic for solving complex optimization problems. It is one of promising optimistic methods which are based on swarm intelligence. SFLA combines the advantages of memetic algorithm and particle swarm optimization and has been widely used in engineering fields. In order to overcome the shortcomings of local search in the classic SFLA, a novel update method with convergence property is presented in this paper. On the basis of the proposed approach, the modified SFLA is presented afterwards. Experimental results show that the efficiency and convergence of the modified SFLA can be enhanced significantly.

Original languageEnglish
Title of host publicationProceedings - 2011 International Conference on Instrumentation, Measurement, Computer, Communication and Control, IMCCC 2011
Pages1016-1019
Number of pages4
DOIs
StatePublished - 2011
Event1st International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC2011 - Beijing, China
Duration: 21 Oct 201123 Oct 2011

Publication series

NameProceedings - 2011 International Conference on Instrumentation, Measurement, Computer, Communication and Control, IMCCC 2011

Conference

Conference1st International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC2011
Country/TerritoryChina
CityBeijing
Period21/10/1123/10/11

Keywords

  • Shuffled frog leaping algorithm
  • computation intelligence
  • swarm intelligence

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