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Three Chaotic Strategies for Enhancing the Self-Adaptive Harris Hawk Optimization Algorithm for Global Optimization

Author

Listed:
  • Sultan Almotairi

    (Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia
    Department of Computer Science, Faculty of Computer and Information Systems, Islamic University of Madinah, Medinah 42351, Saudi Arabia)

  • Elsayed Badr

    (Scientific Computing Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
    Data Science Department, Faculty of Computers and Information Systems, Egyptian Chinese University, Cairo 11786, Egypt)

  • Mustafa Abdul Salam

    (Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt
    Faculty of Computer Studies, Arab Open University, Cairo 11211, Egypt)

  • Alshimaa Dawood

    (Scientific Computing Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13518, Egypt)

Abstract

Harris Hawk Optimization (HHO) is a well-known nature-inspired metaheuristic model inspired by the distinctive foraging strategy and cooperative behavior of Harris Hawks. As with numerous other algorithms, HHO is susceptible to getting stuck in local optima and has a sluggish convergence rate. Several techniques have been proposed in the literature to improve the performance of metaheuristic algorithms (MAs) and to tackle their limitations. Chaos optimization strategies have been proposed for many years to enhance MAs. There are four distinct categories of Chaos strategies, including chaotic mapped initialization, randomness, iterations, and controlled parameters. This paper introduces SHHOIRC, a novel hybrid algorithm designed to enhance the efficiency of HHO. Self-adaptive Harris Hawk Optimization using three chaotic optimization methods (SHHOIRC) is the proposed algorithm. On 16 well-known benchmark functions, the proposed hybrid algorithm, authentic HHO, and five HHO variants are evaluated. The computational results and statistical analysis demonstrate that SHHOIRC exhibits notable similarities to other previously published algorithms. The proposed algorithm outperformed the other algorithms by 81.25%, compared to 18.75% for the prior algorithms, by obtaining the best average solutions for 13 benchmark functions. Furthermore, the proposed algorithm is tested on a real-life problem, which is the maximum coverage problem of Wireless Sensor Networks (WSNs), and compared with pure HHO, and two well-known algorithms, Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). For the maximum coverage experiments, the proposed algorithm demonstrated superior performance, surpassing other algorithms by obtaining the best coverage rates of 95.4375% and 97.125% for experiments 1 and 2, respectively.

Suggested Citation

  • Sultan Almotairi & Elsayed Badr & Mustafa Abdul Salam & Alshimaa Dawood, 2023. "Three Chaotic Strategies for Enhancing the Self-Adaptive Harris Hawk Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 11(19), pages 1-27, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4181-:d:1254444
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    References listed on IDEAS

    as
    1. Tingting Zou & Changyu Wang, 2022. "Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization," Mathematics, MDPI, vol. 10(7), pages 1-19, April.
    2. Yang, Dixiong & Li, Gang & Cheng, Gengdong, 2007. "On the efficiency of chaos optimization algorithms for global optimization," Chaos, Solitons & Fractals, Elsevier, vol. 34(4), pages 1366-1375.
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