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Semi-self-adaptive harmony search algorithm

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Abstract

This paper proposes a semi-self-adaptive harmony search algorithm (SSaHS) with the self-adaptive adjustment of the bandwidth and the elitist learning strategy of particle swarm optimization. SSaHS employs a self-adaptive adjusting strategy for with the difference between the maximum and minimum components in the harmony memory as the bandwidth. It can dynamically adjust the bandwidth for the specific problem to strengthen local exploitation ability and improve the accuracy of optimization results. Comparison results show that the semi-self-adaptive harmony search algorithm can find better solutions when comparing with both basic harmony search algorithm and several enhanced harmony search algorithms, including an improved harmony search, a global-best harmony search and a novel global harmony search.

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Acknowledgements

This research is supported by National Natural Science Foundation of China (61375066, 61374204). We will express our awfully thanks to the Swarm Intelligence Research Team of BeiYou University.

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Authors and Affiliations

  1. School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Xinchao Zhao, Zhaohua Liu & Rui Li

  2. School of Statistics, University of International Business and Economics, Beijing, 10029, China

    Junling Hao

  3. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Xingquan Zuo

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  1. Xinchao Zhao
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  2. Zhaohua Liu
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  3. Junling Hao
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  4. Rui Li
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  5. Xingquan Zuo
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Correspondence to Xinchao Zhao.

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Zhao, X., Liu, Z., Hao, J. et al. Semi-self-adaptive harmony search algorithm. Nat Comput 16, 619–636 (2017). https://doi.org/10.1007/s11047-017-9614-5

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