Fully connected multi-objective particle swarm optimizer based on neural network

Zenghui Wang, Yanxia Sun

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


In this paper, a new model for multi-objective particle swarm optimization (MOPSO) is proposed. In this model, each particle's behavior is influenced by the best experience among its neighbors, its own best experience and all its components. The influence among different components of particles is implemented by the on-line training of a multi-input Multi-output back propagation (BP) neural network. The inputs and outputs of the BP neural network are the particle position and its the 'gradient descent' direction vector to the less objective value according to the definition of no-domination, respectively. Therefore, the new structured MOPSO model is called a fully connected multi-objective particle swarm optimizer (FCMOPSO). Simulation results and comparisons with exiting MOPSOs demonstrate that the proposed FCMOPSO is more stable and can improve the optimization performance.

Original languageEnglish
Title of host publicationAdvanced Intelligent Computing - 7th International Conference, ICIC 2011, Revised Selected Papers
Number of pages8
Publication statusPublished - 2011
Externally publishedYes
Event7th International Conference on Intelligent Computing, ICIC 2011 - Zhengzhou, China
Duration: 11 Aug 201114 Aug 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6838 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Intelligent Computing, ICIC 2011


  • Multi-objective Optimization
  • Neural Network
  • Pareto Front
  • Particle Swarm Optimization
  • non-domination

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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