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

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Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 862))

Abstract

A fuzzy harmony search algorithm (FHS) is presented in this paper. This method uses a fuzzy system for dynamic adaptation of the harmony memory accepting (HMR) and pitch adjustment (PArate) parameters along the iterations, and in this way achieving control of the intensification and diversification of the search space. This method was previously applied to various benchmark controller cases however in this case we decided to apply the proposed FHS to benchmark controller problem with different types of noise: band-limited white noise, pulse noise, and uniform random number noise to check the efficiency for the pro-posed method. A comparison is presented to verify the results obtained with the original harmony search algorithm and 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 Fevrier Valdez .

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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

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Peraza, C., Valdez, F., Castillo, O. (2020). Harmony Search with Dynamic Adaptation of Parameters for the Optimization of a Benchmark Controller. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 862. Springer, Cham. https://doi.org/10.1007/978-3-030-35445-9_14

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