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Improved harmony search with general iteration models for engineering design optimization problems

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Abstract

Harmony search (HS) algorithm has a strong exploration and exploitation capability based on its unique improvisation. However, little research has been done on its improvisation mechanism. This paper offers a detailed discussion on the HS improvisation mechanism, which aims to state that the improvisation is a generic search framework. To improve the performance of HS, global learning strategy is designed to enhance the global search capability, and modified random selection is used to reduce the possibility of falling into local optimum. Moreover, a new improvement perspective such as the adjustment of iteration model is presented in this paper. Different iteration models such as dimension-to-dimension mode, stochastic multi-dimensional mode, vector mode and matrix mode to explore the optimization potential of HS algorithm are employed. Combining the improved operations, parameter adjustments and the four iteration models, four improved HS variants are proposed to analyze the effectiveness of iteration model on HS algorithm. Experimental results demonstrate the proposed HS algorithms can yield significant improved performance. Overall, the paper shows that the HS improvisation framework has a good extensibility and the iteration model has significant impact on the performance of HS.

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Acknowledgements

The authors are grateful to the editor and the anonymous referees for their constructive comments and recommendations, which have helped to improve this paper significantly. The authors would also like to express their sincere thanks to P. N. Suganthan for the useful information about meta-heuristic algorithm and optimization problems on their home webpages. This work is supported by Guangzhou Science and Technology Plan Project (201804010299), National Nature Science Foundation of China (Grant No. 61806058, 51775122, 61603105) and 2017 Undergraduate Innovation Training Program of Guangzhou University (201711078068, CX2017070), major science and technology projects of Guangdong province (2016B090912007) and Guangzhou university talent launch program (2700050326).

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Authors and Affiliations

  1. School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou, 510006, China

    Haibin Ouyang, Wenqiang Wu, Chunliang Zhang & Guiyun Liu

  2. Graduate School of Business and Law, RMIT University, Melbourne, 3000, Australia

    Steven Li

  3. School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, 221116, China

    Dexuan Zou

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  1. Haibin Ouyang
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Correspondence to Haibin Ouyang.

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Communicated by V. Loia.

Wenqiang Wu is the co-first author.

Appendix

Appendix

See Figs. 14 and 15.

Fig. 14
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Evolution of the optimum objective function value for the median run of five algorithms for f1–f16 (D = 30)

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Fig. 15
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Evolution of the optimum objective function value for the median run of five algorithms for f1–f16 (D = 100)

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Ouyang, H., Wu, W., Zhang, C. et al. Improved harmony search with general iteration models for engineering design optimization problems. Soft Comput 23, 10225–10260 (2019). https://doi.org/10.1007/s00500-018-3579-x

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