A Collaborative Neurodynamic Approach to Multiple-Objective Distributed Optimization

Shaofu Yang, Qingshan Liu, Jun Wang

Research output: Contribution to journalArticlepeer-review

81 Citations (Scopus)

Abstract

This paper is concerned with multiple-objective distributed optimization. Based on objective weighting and decision space decomposition, a collaborative neurodynamic approach to multiobjective distributed optimization is presented. In the approach, a system of collaborative neural networks is developed to search for Pareto optimal solutions, where each neural network is associated with one objective function and given constraints. Sufficient conditions are derived for ascertaining the convergence to a Pareto optimal solution of the collaborative neurodynamic system. In addition, it is proved that each connected subsystem can generate a Pareto optimal solution when the communication topology is disconnected. Then, a switching-topology-based method is proposed to compute multiple Pareto optimal solutions for discretized approximation of Pareto front. Finally, simulation results are discussed to substantiate the performance of the collaborative neurodynamic approach. A portfolio selection application is also given.

Original languageEnglish
Pages (from-to)981-992
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume29
Issue number4
DOIs
Publication statusPublished - Apr 2018
Externally publishedYes

Keywords

  • Collaborative neurodynamic approach
  • distributed optimization
  • multiobjective optimization
  • neural networks
  • Pareto optimal solutions

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