A new hybrid PSOGSA algorithm for function optimization

Seyedali Mirjalili, Siti Zaiton Mohd Hashim

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

314 Citations (Scopus)

Abstract

In this paper, a new hybrid population-based algorithm (PSOGSA) is proposed with the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). The main idea is to integrate the ability of exploitation in PSO with the ability of exploration in GSA to synthesize both algorithms' strength. Some benchmark test functions are used to compare the hybrid algorithm with both the standard PSO and GSA algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard PSO and GSA.

Original languageEnglish
Title of host publicationProceedings of ICCIA 2010 - 2010 International Conference on Computer and Information Application
Pages374-377
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2010
Externally publishedYes
Event2010 International Conference on Computer and Information Application, ICCIA 2010 - Tianjin, China
Duration: 2 Nov 20104 Nov 2010

Publication series

NameProceedings of ICCIA 2010 - 2010 International Conference on Computer and Information Application

Conference

Conference2010 International Conference on Computer and Information Application, ICCIA 2010
CountryChina
CityTianjin
Period2/11/104/11/10

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Keywords

  • Function optimization
  • Gravitational Search Algorithm (GSA)
  • Particle Swarm Optimization (PSO)

Cite this

Mirjalili, S., & Hashim, S. Z. M. (2010). A new hybrid PSOGSA algorithm for function optimization. In Proceedings of ICCIA 2010 - 2010 International Conference on Computer and Information Application (pp. 374-377). [6141614] (Proceedings of ICCIA 2010 - 2010 International Conference on Computer and Information Application). https://doi.org/10.1109/ICCIA.2010.6141614