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Harmony Search Algorithm and Fuzzy Logic Theory: An Extensive Review from Theory to Applications

Author

Listed:
  • Mohammad Nasir

    (Materials and Energy Research Center, Dezful Branch, Islamic Azad University, Dezful, Iran)

  • Ali Sadollah

    (Department of Mechanical Engineering, University of Science and Culture, Tehran, Iran)

  • Przemyslaw Grzegorzewski

    (Faculty of Mathematics and Information Science, Warsaw University of Technology, Koszykowa 75, 00-662 Warsaw, Poland
    Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447 Warsaw, Poland)

  • Jin Hee Yoon

    (School of Mathematics and Statistics, Sejong University, Seoul 05006, Korea)

  • Zong Woo Geem

    (College of IT Convergence, Gachon University, Seongnam 13120, Korea)

Abstract

In recent years, many researchers have utilized metaheuristic optimization algorithms along with fuzzy logic theory in their studies for various purposes. The harmony search (HS) algorithm is one of the metaheuristic optimization algorithms that is widely employed in different studies along with fuzzy logic (FL) theory. FL theory is a mathematical approach to expressing uncertainty by applying the conceptualization of fuzziness in a system. This review paper presents an extensive review of published papers based on the combination of HS and FL systems. In this regard, the functional characteristics of models obtained from integration of FL and HS have been reported in various articles, and the performance of each study is investigated. The basic concept of the FL approach and its derived models are introduced to familiarize readers with the principal mechanisms of FL models. Moreover, appropriate descriptions of the primary classifications acquired from the coexistence of FL and HS methods for specific purposes are reviewed. The results show that the high efficiency of HS to improve the exploration of FL in achieving the optimal solution on the one hand, and the capability of fuzzy inference systems to provide more flexible and dynamic adaptation of the HS parameters based on human perception on the other hand, can be a powerful combination for solving optimization problems. This review paper is believed to be a useful resource for students, engineers, and professionals.

Suggested Citation

  • Mohammad Nasir & Ali Sadollah & Przemyslaw Grzegorzewski & Jin Hee Yoon & Zong Woo Geem, 2021. "Harmony Search Algorithm and Fuzzy Logic Theory: An Extensive Review from Theory to Applications," Mathematics, MDPI, vol. 9(21), pages 1-46, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2665-:d:661706
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    References listed on IDEAS

    as
    1. Zhai, Pei & Williams, Eric D., 2012. "Analyzing consumer acceptance of photovoltaics (PV) using fuzzy logic model," Renewable Energy, Elsevier, vol. 41(C), pages 350-357.
    2. Mahima Dubey & Vijay Kumar & Manjit Kaur & Thanh-Phong Dao, 2021. "A Systematic Review on Harmony Search Algorithm: Theory, Literature, and Applications," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-22, April.
    3. Yan Hong Chen & Wei-Chiang Hong & Wen Shen & Ning Ning Huang, 2016. "Electric Load Forecasting Based on a Least Squares Support Vector Machine with Fuzzy Time Series and Global Harmony Search Algorithm," Energies, MDPI, vol. 9(2), pages 1-13, January.
    4. Meena, Rakesh Kumar & Jain, Madhu & Sanga, Sudeep Singh & Assad, Assif, 2019. "Fuzzy modeling and harmony search optimization for machining system with general repair, standby support and vacation," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 858-873.
    5. Kai Zhou Gao & Ponnuthurai Nagaratnam Suganthan & Quan Ke Pan & Mehmet Fatih Tasgetiren, 2015. "An effective discrete harmony search algorithm for flexible job shop scheduling problem with fuzzy processing time," International Journal of Production Research, Taylor & Francis Journals, vol. 53(19), pages 5896-5911, October.
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    Cited by:

    1. Man-Wen Tian & Shu-Rong Yan & Jinping Liu & Khalid A. Alattas & Ardashir Mohammadzadeh & Mai The Vu, 2022. "A New Type-3 Fuzzy Logic Approach for Chaotic Systems: Robust Learning Algorithm," Mathematics, MDPI, vol. 10(15), pages 1-20, July.

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