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Harmony Search with Dynamic Adaptation of Parameters for the Optimization of a Benchmark Set of Functions

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Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

Part of the book series: Studies in Computational Intelligence ((SCI,volume 827))

Abstract

In this paper a fuzzy search algorithm harmony (FHS) is presented. The main difference between previous work is that this method uses a fuzzy system for dynamic parameter adaptation of the two main parameters throughout the iterations of the algorithm, which are: harmony memory accepting (HMR) and pitch adjustment (PArate), with the rules of the fuzzy system control the intensification and diversification of the search space is achieved. This method was applied to the mathematical functions provided by the CEC 2017, which are unimodal, multimodal, hybrid and composite functions to verify the efficiency of the proposed method. A comparison is presented to verify the results obtained with the original harmony search algorithm and the fuzzy harmony search algorithm.

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Acknowledgements

We would like to express our thanks to CONACYT and Tijuana Institute of Technology for the facilities and resources granted for the development of this research.

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Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, BC, Mexico

    Cinthia Peraza, Fevrier Valdez & Oscar Castillo

Authors
  1. Cinthia Peraza
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  2. Fevrier Valdez
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  3. Oscar Castillo
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Corresponding author

Correspondence to Oscar Castillo .

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Editors and Affiliations

  1. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Patricia Melin

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Peraza, C., Valdez, F., Castillo, O. (2020). Harmony Search with Dynamic Adaptation of Parameters for the Optimization of a Benchmark Set of Functions. In: Castillo, O., Melin, P. (eds) Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine. Studies in Computational Intelligence, vol 827. Springer, Cham. https://doi.org/10.1007/978-3-030-34135-0_8

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