Neural network with migration parallel ga for adaptive control of integrated DE-PSO parameters

Hieu Pham, Sousuke Tooyama, Hiroshi Hasegawa

研究成果: Conference contribution

抄録

This study develops an evolutionary strategy called DEPSO-GANN, which uses an artificial neural network (ANN) based on a parallel genetic algorithm (PGA) with migration for the adaptive control of integrated differential evolution (DE) and particle swarm optimization (PSO) to solve large-scale optimization problems, reduce calculation costs, and improve the stability of convergence towards the optimal solution. This approach combines the global search ability of DE and the local search ability of adaptive system with migration parallel GA. The proposed algorithm incorporates concepts from DE, PSO, PGA and neural networks (NN) to facilitate the adaptive control of parameters. DEPSO-GANN is applied to several numerical benchmark tests with multiple dimensions to evaluate its performance, it is also compared with other evolutionary algorithms (EAs) and memetic algorithms (MAs), which is shown to be statistically significantly superior to other EAs and MAs. We confirm satisfactory performance through various benchmark tests.

本文言語English
ホスト出版物のタイトルProceedings - 8th EUROSIM Congress on Modelling and Simulation, EUROSIM 2013
編集者David Al-Dabass, Richard Cant, Richard Zobel, Khalid Al-Begain, Richard Cant, Alessandra Orsoni, Khalid Al-Begain, Alessandra Orsoni, David Al-Dabass
出版社Institute of Electrical and Electronics Engineers Inc.
ページ13-18
ページ数6
ISBN(電子版)9780769550732
DOI
出版ステータスPublished - 2015 1 8
イベント8th EUROSIM Congress on Modelling and Simulation, EUROSIM 2013 - Cardiff, Wales, United Kingdom
継続期間: 2013 9 102013 9 13

出版物シリーズ

名前Proceedings - 8th EUROSIM Congress on Modelling and Simulation, EUROSIM 2013

Other

Other8th EUROSIM Congress on Modelling and Simulation, EUROSIM 2013
CountryUnited Kingdom
CityCardiff, Wales
Period13/9/1013/9/13

ASJC Scopus subject areas

  • Modelling and Simulation
  • Computational Theory and Mathematics
  • Computer Science Applications

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