Hybrid approach for improved particle swarm optimization using Adaptive plan system with genetic algorithm

Pham Ngoc Hieu, Hiroshi Hasegawa

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

Abstract

To reduce a large amount of calculation cost and to improve the convergence to the optimal solution for multi-peak optimization problems with multi-dimensions, we purpose a new method of Adaptive plan system with Genetic Algorithm (APGA). This is an approach for Improved Particle Swarm Optimization (PSO) using APGA. The hybrid strategy using APGA is introduced into PSO operator (H-PSOGA) to improve the convergence towards the optimal solution. The H-PSOGA is applied to some benchmark functions with 20 dimensions to evaluate its performance.

Original languageEnglish
Title of host publicationECTA 2011 FCTA 2011 - Proceedings of the International Conference on Evolutionary Computation Theory and Applications and International Conference on Fuzzy Computation Theory and Applications
Pages267-272
Number of pages6
Publication statusPublished - 2011 Dec 1
EventInternational Conference on Evolutionary Computation Theory and Applications, ECTA 2011 and International Conference on Fuzzy Computation Theory and Applications, FCTA 2011 - Paris, France
Duration: 2011 Oct 242011 Oct 26

Publication series

NameECTA 2011 FCTA 2011 - Proceedings of the International Conference on Evolutionary Computation Theory and Applications and International Conference on Fuzzy Computation Theory and Applications

Conference

ConferenceInternational Conference on Evolutionary Computation Theory and Applications, ECTA 2011 and International Conference on Fuzzy Computation Theory and Applications, FCTA 2011
CountryFrance
CityParis
Period11/10/2411/10/26

Keywords

  • Adaptive system
  • Genetic algorithms (GAs)
  • Multi-peak problems
  • Particle Swarm Optimization (PSO)

ASJC Scopus subject areas

  • Applied Mathematics

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  • Cite this

    Hieu, P. N., & Hasegawa, H. (2011). Hybrid approach for improved particle swarm optimization using Adaptive plan system with genetic algorithm. In ECTA 2011 FCTA 2011 - Proceedings of the International Conference on Evolutionary Computation Theory and Applications and International Conference on Fuzzy Computation Theory and Applications (pp. 267-272). (ECTA 2011 FCTA 2011 - Proceedings of the International Conference on Evolutionary Computation Theory and Applications and International Conference on Fuzzy Computation Theory and Applications).