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Survey of Lévy Flight-Based Metaheuristics for Optimization

Author

Listed:
  • Juan Li

    (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)

  • Qing An

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

  • Hong Lei

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

  • Qian Deng

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

  • Gai-Ge Wang

    (Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
    Intelligent Information Systems Institute, Wenzhou University, Wenzhou 325035, China
    Key Laboratory of Intelligent Image Processing and Analysis, Wenzhou 325035, China)

Abstract

Lévy flight is a random walk mechanism which can make large jumps at local locations with a high probability. The probability density distribution of Lévy flight was characterized by sharp peaks, asymmetry, and trailing. Its movement pattern alternated between frequent short-distance jumps and occasional long-distance jumps, which can jump out of local optimal and expand the population search area. The metaheuristic algorithms are inspired by nature and applied to solve NP-hard problems. Lévy flight is used as an operator in the cuckoo algorithm, monarch butterfly optimization, and moth search algorithms. The superiority for the Lévy flight-based metaheuristic algorithms has been demonstrated in many benchmark problems and various application areas. A comprehensive survey of the Lévy flight-based metaheuristic algorithms is conducted in this paper. The research includes the following sections: statistical analysis about Lévy flight, metaheuristic algorithms with a Lévy flight operator, and classification of Lévy flight used in metaheuristic algorithms. The future insights and development direction in the area of Lévy flight are also discussed.

Suggested Citation

  • Juan Li & Qing An & Hong Lei & Qian Deng & Gai-Ge Wang, 2022. "Survey of Lévy Flight-Based Metaheuristics for Optimization," Mathematics, MDPI, vol. 10(15), pages 1-27, August.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2785-:d:881515
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    References listed on IDEAS

    as
    1. Anbang Wang & Lihong Guo & Yuan Chen & Junjie Wang & Luo Liu & Yuanzhang Song, 2021. "An Improved Cuckoo Search Algorithm With Stud Crossover for Chinese TSP Problem," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(4), pages 1-26, October.
    2. Jiajun Zhou & Xifan Yao, 2017. "A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4765-4784, August.
    3. Ni, Yulong & Xu, Jianing & Zhu, Chunbo & Pei, Lei, 2022. "Accurate residual capacity estimation of retired LiFePO4 batteries based on mechanism and data-driven model," Applied Energy, Elsevier, vol. 305(C).
    4. Dash, Deepak Ranjan & Dash, P.K. & Bisoi, Ranjeeta, 2021. "Short term solar power forecasting using hybrid minimum variance expanded RVFLN and Sine-Cosine Levy Flight PSO algorithm," Renewable Energy, Elsevier, vol. 174(C), pages 513-537.
    5. Minsheng Yang & Jianqi Li & Jianying Li & Xiaofang Yuan & Jiazhu Xu, 2021. "Reconfiguration Strategy for DC Distribution Network Fault Recovery Based on Hybrid Particle Swarm Optimization," Energies, MDPI, vol. 14(21), pages 1-15, November.
    6. Yan, Zheping & Zhang, Jinzhong & Zeng, Jia & Tang, Jialing, 2021. "Nature-inspired approach: An enhanced whale optimization algorithm for global optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 17-46.
    7. Juan Li & Dan-dan Xiao & Hong Lei & Ting Zhang & Tian Tian, 2020. "Using Cuckoo Search Algorithm with Q -Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location," Mathematics, MDPI, vol. 8(2), pages 1-32, January.
    8. Ren, Hao & Li, Jun & Chen, Huiling & Li, ChenYang, 2021. "Adaptive levy-assisted salp swarm algorithm: Analysis and optimization case studies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 181(C), pages 380-409.
    9. Fathy, Ahmed & Elaziz, Mohamed Abd & Sayed, Enas Taha & Olabi, A.G. & Rezk, Hegazy, 2019. "Optimal parameter identification of triple-junction photovoltaic panel based on enhanced moth search algorithm," Energy, Elsevier, vol. 188(C).
    10. Yang Zhang & Huihui Zhao & Yuming Cao & Qinhuo Liu & Zhanfeng Shen & Jian Wang & Minggang Hu, 2018. "A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering," Energies, MDPI, vol. 11(10), pages 1-23, October.
    11. Minhee Kim & Junjae Chae, 2019. "Monarch Butterfly Optimization for Facility Layout Design Based on a Single Loop Material Handling Path," Mathematics, MDPI, vol. 7(2), pages 1-21, February.
    12. Xuan Chen & Feng Cheng & Cong Liu & Long Cheng & Yin Mao, 2021. "An improved Wolf pack algorithm for optimization problems: Design and evaluation," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-23, August.
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