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Elephant Herding Optimization: Variants, Hybrids, and Applications

Author

Listed:
  • Juan Li

    (School of Artificial Intelligence, Wuhan Technology and Business University, Wuhan 430065, China
    School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China
    Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China)

  • Hong Lei

    (School of Artificial Intelligence, Wuchang University of Technology, Wuhan 430223, China)

  • Amir H. Alavi

    (Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
    Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15261, USA
    Department of Computer Science and Information Engineering, Asia University, Taichung 41354, Taiwan)

  • Gai-Ge Wang

    (Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
    Institute of Algorithm and Big Data Analysis, Northeast Normal University, Changchun 130117, China
    School of Computer Science and Information Technology, Northeast Normal University, Changchun 130117, China)

Abstract

Elephant herding optimization (EHO) is a nature-inspired metaheuristic optimization algorithm based on the herding behavior of elephants. EHO uses a clan operator to update the distance of the elephants in each clan with respect to the position of a matriarch elephant. The superiority of the EHO method to several state-of-the-art metaheuristic algorithms has been demonstrated for many benchmark problems and in various application areas. A comprehensive review for the EHO-based algorithms and their applications are presented in this paper. Various aspects of the EHO variants for continuous optimization, combinatorial optimization, constrained optimization, and multi-objective optimization are reviewed. Future directions for research in the area of EHO are further discussed.

Suggested Citation

  • Juan Li & Hong Lei & Amir H. Alavi & Gai-Ge Wang, 2020. "Elephant Herding Optimization: Variants, Hybrids, and Applications," Mathematics, MDPI, vol. 8(9), pages 1-25, August.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1415-:d:403101
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    Citations

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    Cited by:

    1. Meshari Alsharari & Ammar Armghan & Khaled Aliqab, 2023. "Numerical Analysis and Parametric Optimization of T-Shaped Symmetrical Metasurface with Broad Bandwidth for Solar Absorber Application Based on Graphene Material," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    2. Chen, Xiao & Cao, Benyi & Pouramini, Somayeh, 2023. "Energy cost and consumption reduction of an office building by Chaotic Satin Bowerbird Optimization Algorithm with model predictive control and artificial neural network: A case study," Energy, Elsevier, vol. 270(C).
    3. Mohamed Abdel-Basset & Reda Mohamed & Karam M. Sallam & Ripon K. Chakrabortty, 2022. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm," Mathematics, MDPI, vol. 10(19), pages 1-63, September.
    4. Juan Li & Yuan-Hua Yang & Qing An & Hong Lei & Qian Deng & Gai-Ge Wang, 2022. "Moth Search: Variants, Hybrids, and Applications," Mathematics, MDPI, vol. 10(21), pages 1-19, November.
    5. Hsieh, Tsung-Jung, 2023. "A Q-learning guided search for developing a hybrid of mixed redundancy strategies to improve system reliability," Reliability Engineering and System Safety, Elsevier, vol. 236(C).

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