A novel hybrid genetic algorithm for global optimization

Shuihua Wang, Lenan Wu*

*Corresponding author for this work

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

3 Citations (Scopus)

Abstract

In order to propose a more effective function optimization method, a novel algorithm named HGPSA was proposed which integrates the powerful global search ability of GA and the excellent local search ability of PS. The experiments of 10 runs on three test functions (Powell function, Rosenbrock function, and Schaffer function) demonstrate that the proposed algorithm is superior to both GA and PS with respect to the successful rate. Therefore, the proposed algorithm is valid.

Original languageEnglish
Title of host publicationProceedings - 2010 6th International Conference on Natural Computation, ICNC 2010
Pages1058-1061
Number of pages4
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 6th International Conference on Natural Computation, ICNC'10 - Yantai, Shandong, China
Duration: 10 Aug 201012 Aug 2010

Publication series

NameProceedings - 2010 6th International Conference on Natural Computation, ICNC 2010
Volume2

Conference

Conference2010 6th International Conference on Natural Computation, ICNC'10
Country/TerritoryChina
CityYantai, Shandong
Period10/08/1012/08/10

Keywords

  • Genetic algorithm
  • Global optimization
  • Pattern search

Fingerprint

Dive into the research topics of 'A novel hybrid genetic algorithm for global optimization'. Together they form a unique fingerprint.

Cite this