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An M-Objective Penalty Function Algorithm Under Big Penalty Parameters

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Abstract

Some classical penalty function algorithms may not always be convergent under big penalty parameters in Matlab software, which makes them impossible to find out an optimal solution to constrained optimization problems. In this paper, a novel penalty function (called M-objective penalty function) with one penalty parameter added to both objective and constrained functions of inequality constrained optimization problems is proposed. Based on the M-objective penalty function, an algorithm is developed to solve an optimal solution to the inequality constrained optimization problems, with its convergence proved under some conditions. Furthermore, numerical results show that the proposed algorithm has a much better convergence than the classical penalty function algorithms under big penalty parameters, and is efficient in choosing a penalty parameter in a large range in Matlab software.

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Authors and Affiliations

  1. Basic College, Ningbo Dahongying University, Ningbo, 315175, China

    Ying Zheng

  2. College of Economics and Management, Zhejiang University of Technology, Hangzhou, 310023, China

    Zhiqing Meng & Rui Shen

Authors
  1. Ying Zheng
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  2. Zhiqing Meng
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  3. Rui Shen
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Corresponding author

Correspondence to Ying Zheng.

Additional information

This research was supported by the National Natural Science Foundation of China under Grant No. 11271329.

This paper was recommended for publication by Editor DAI Yuhong.

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Zheng, Y., Meng, Z. & Shen, R. An M-Objective Penalty Function Algorithm Under Big Penalty Parameters. J Syst Sci Complex 29, 455–471 (2016). https://doi.org/10.1007/s11424-015-3204-3

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  • DOI: https://doi.org/10.1007/s11424-015-3204-3

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