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Author:

Han, Hong-Gui (Han, Hong-Gui.) (Scholars:韩红桂) | Wu, Shu-Jun (Wu, Shu-Jun.)

Indexed by:

EI Scopus PKU CSCD

Abstract:

To determine the population size of multi-objective particle swarm optimization algorithm (MOPSO), an improved MOPSO, based on the convergence speed and diversity, named CD-MOPSO, is proposed. Firstly, the fitness function of population size, which is developed by the convergence speed and diversity during the evolutionary process, is used to describe the relationship between the population size and the performance of MOPSO. Secondly, according to the fitness function, an adaptive adjustment method is designed to update the population size of MOPSO dynamically. Finally, the proposed CD-MOPSO is tested on the ZDT benchmark optimization problems and applied to a real optimization problem of urban pipe networks. The experimental results show that the proposed CD-MOPSO can adjust the population size automatically according to the problem, compared with the performance of NSGA, MOPSO, SPEA2 and EMDS-MOPSO, CD-MOPSO has faster convergence speed with better optimization results. © 2018, Chinese Institute of Electronics. All right reserved.

Keyword:

Multiobjective optimization Benchmarking Population statistics Health Speed Particle swarm optimization (PSO)

Author Community:

  • [ 1 ] [Han, Hong-Gui]Department of Information, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Han, Hong-Gui]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 3 ] [Wu, Shu-Jun]Department of Information, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Wu, Shu-Jun]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China

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Source :

Acta Electronica Sinica

ISSN: 0372-2112

Year: 2018

Issue: 9

Volume: 46

Page: 2263-2269

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 25

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