Hybrid particle swarm and conjugate gradient optimization algorithm

Abdallah Qteish, Mohammad Hamdan

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

3 Citations (Scopus)

Abstract

In this work we propose a different particle swarm optimization (PSO) algorithm that employs two key features of the conjugate gradient (CG) method. Namely, adaptive weight factor for each particle and iteration number (calculated as in the CG approach), and periodic restart. Experimental results for four well known test problems have showed the superiority of the new PSO-CG approach, compared with the classical PSO algorithm, in terms of convergence speed and quality of obtained solutions.

Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence. ICSI 2010
PublisherSpringer
Pages582-588
Number of pages7
ISBN (Electronic)9783642134951
ISBN (Print)9783642134944
DOIs
Publication statusPublished - 2010
Event1st International Conference on Advances in Swarm Intelligence 2010 - Beijing, China
Duration: 12 Jun 201015 Jun 2010

Publication series

NameLecture Notes in Computer Science
Volume6145
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Conference on Advances in Swarm Intelligence 2010
Abbreviated titleICSI 2010
CountryChina
CityBeijing
Period12/06/1015/06/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Qteish, A., & Hamdan, M. (2010). Hybrid particle swarm and conjugate gradient optimization algorithm. In Advances in Swarm Intelligence. ICSI 2010 (pp. 582-588). (Lecture Notes in Computer Science; Vol. 6145). Springer. https://doi.org/10.1007/978-3-642-13495-1_71