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A Neurodynamic Approach to Multiobjective Linear Programming

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Advances in Neural Networks – ISNN 2018 (ISNN 2018)

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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.

This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China, under Grants 14207614 and 11208517, and in part by the National Natural Science Foundation of China under grant 61673330.

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Author information

Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

    Man-Fai Leung & Jun Wang

  2. City University of Hong Kong, Shenzhen, Guangdong, China

    Man-Fai Leung & Jun Wang

Authors
  1. Man-Fai Leung
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  2. Jun Wang
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Corresponding author

Correspondence to Man-Fai Leung .

Editor information

Editors and Affiliations

  1. Texas A&M University at Qatar, Doha, Qatar

    Tingwen Huang

  2. Sichuan University, Chengdu, China

    Jiancheng Lv

  3. Southeast University, Nanjing, China

    Changyin Sun

  4. United Institute of Informatics Problems, Minsk, Belarus

    Alexander V. Tuzikov

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Leung, MF., Wang, J. (2018). A Neurodynamic Approach to Multiobjective Linear Programming. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-92537-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92536-3

  • Online ISBN: 978-3-319-92537-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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