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A novel harmony search algorithm with gaussian mutation for multi-objective optimization

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Abstract

This paper proposes a novel harmony search algorithm with gaussian mutation (GMHS) for multi-objective optimization. Harmony search (HS) algorithm has shown many advantages in solving global optimization problems; however, it also has some shortcomings, such as poor ability of escaping from local optimum and poor convergence. In view of the weaknesses of HS algorithm, several important improvements are employed in the proposed GMHS, including (a) the memory consideration rule is modified to improve convergence, (b) two bandwidths in pitch adjustment are designed to obtain better exploration and exploitation, (c) chaotic maps are utilized in the GMHS to enhance global search ability and (d) a gaussian mutation operator is employed in the GMHS to speed up convergence rate and to jump out the local optimum. To solve multi-objective optimization problems, the GMHS uses fast non-dominated sorting and crowding distance method to update harmony memory. For the purpose of preserving non-dominated solutions found during the entire search process, an external archive has been adopted. To demonstrate the effectiveness of the GMHS, it is tested with benchmark problems. The experimental results show that the GMHS is competitive in convergence and diversity performance, compared with other multi-objective evolutionary algorithms. Finally, the impact of two key parameters on the performance of GMHS is also analyzed.

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Xiangshan Dai & Xiaofang Yuan

  2. College of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Lianghong Wu

Authors
  1. Xiangshan Dai
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  2. Xiaofang Yuan
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  3. Lianghong Wu
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Corresponding author

Correspondence to Xiaofang Yuan.

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Conflict of interest

No conflict of interest exits in the publication of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed.

Additional information

Communicated by V. Loia.

This work was supported in part by the National Natural Science Foundation of China (No. 61573133, No. 61203309) and Hunan Provincial Natural Science Foundation of China (No. 2015JJ3053).

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Dai, X., Yuan, X. & Wu, L. A novel harmony search algorithm with gaussian mutation for multi-objective optimization. Soft Comput 21, 1549–1567 (2017). https://doi.org/10.1007/s00500-015-1868-1

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