A Neurodynamic Approach to Multiobjective Linear Programming

Man-Fai Leung*, Jun Wang

*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

7 Citations (Scopus)

Abstract

In this paper, a neurodynamic approach is proposed for solving multiobjective linear programming problems. Multiple objectives are firstly scalarized using a weighted sum technique. Recurrent neural networks are then adopted to generate Pareto-optimal solutions. To diversify the solutions along Pareto fronts, particle swarm optimization is used to optimize the weights of the scalarized objective function. Numerical results are presented to illustrate the effectiveness of the proposed approaches.
Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2018
Subtitle of host publication15th International Symposium on Neural Networks, ISNN 2018, Proceedings
EditorsTingwen Huang, Jiancheng Lv, Changyin Sun , Alexander V. Tuzikov
PublisherSpringer Verlag
Pages11-18
ISBN (Electronic)9783319925370
ISBN (Print)9783319925363
DOIs
Publication statusPublished - Jun 2018
Event15th International Symposium on Neural Networks (ISNN 2018) - Minsk, Belarus
Duration: 25 Jun 201828 Jun 2018

Publication series

NameLecture Notes in Computer Science
Volume10878
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Symposium on Neural Networks (ISNN 2018)
PlaceBelarus
CityMinsk
Period25/06/1828/06/18

Research Keywords

  • Linear programming
  • Multiobjective optimization
  • Neurodynamic optimization
  • Recurrent neural network

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