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An effective and efficient two stage algorithm for global optimization

  • Xi'an Jiaotong University
  • Hebei University

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

2 Scopus citations

Abstract

A two stage algorithm, consisting of gradient technique and panicle swarm optimization (PSO) method for global optimization is proposed. The gradient method is used to find a local minimum of objective function efficiently, and PSO with potential parallel search is employed to help the minimization sequence to escape from the previously converged local minima to a better point which is then given to the gradient method as a starting point to start a new local search. The above search procedure is applied repeatedly until a global minimum of the objective function is found. In addition, a repulsion technique and partially initializing population method are also incorporated in the new algorithm to increase its global search ability. Global convergence is proven, and tests on benchmark problems show that the proposed method is more effective and reliable than the existing optimization methods.

Original languageEnglish
Title of host publicationAdvances in Machine Learning and Cybernetics - 4th International Conference, ICMLC 2005, Revised Selected Papers
Pages487-496
Number of pages10
DOIs
StatePublished - 2006
Event4th International Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China
Duration: 18 Aug 200521 Aug 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3930 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Conference on Machine Learning and Cybernetics, ICMLC 2005
Country/TerritoryChina
CityGuangzhou
Period18/08/0521/08/05

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