Differential evolution for adaptive system of particle swarm optimization with genetic algorithm

Pham Ngoc Hieu, Hiroshi Hasegawa

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

1 Citation (Scopus)

Abstract

A new strategy using Differential Evolution (DE) for Adaptive Plan System of Particle Swarm Optimization (PSO) with Genetic Algorithm (GA) called DE-PSO-APGA is proposed to solve a huge scale optimization problem, and to improve the convergence towards the optimal solution. This is an approach that combines the global search ability of GA and Adaptive plan (AP) for local search ability. The proposed strategy incorporates concepts from DE and PSO, updating particles not only by DE operators but also by mechanism of PSO for Adaptive System (AS). The DE-PSO-APGA is applied to several benchmark functions with multi-dimensions to evaluate its performance. We confirmed satisfactory performance through various benchmark tests.

Original languageEnglish
Title of host publicationIJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence
Pages259-264
Number of pages6
Publication statusPublished - 2012
Event4th International Joint Conference on Computational Intelligence, IJCCI 2012 - Barcelona, Spain
Duration: 2012 Oct 52012 Oct 7

Publication series

NameIJCCI 2012 - Proceedings of the 4th International Joint Conference on Computational Intelligence

Conference

Conference4th International Joint Conference on Computational Intelligence, IJCCI 2012
Country/TerritorySpain
CityBarcelona
Period12/10/512/10/7

Keywords

  • Adaptive System
  • Differential Evolution (DE)
  • Genetic Algorithm (GA)
  • Multi-peak Problems
  • Particle Swarm Optimization (PSO)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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