Elephant Herding Optimization for Benchmark Problems: Algorithm ‎Design, Implementation, and Performance Analysis

  • Authors

    • Renas Rajab Asaad Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, Iraq
    • Awaz Ahmed Shaban Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, Iraq
    • Saman M. Almufti Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, Iraq
    https://doi.org/10.14419/vc2c7252

    Received date: September 14, 2025

    Accepted date: October 21, 2025

    Published date: October 24, 2025

  • Elephant Herding Optimization; Metaheuristic Algorithms; Benchmark Functions; Nature-Inspired Optimization; Convergence ‎Performance; Improved EHO; Large-Scale Optimization.

  • Abstract

    This paper presents an in-depth study on the development and evaluation of an Elephant Herding Optimization (EHO) algorithm tailored to ‎solve a set of standard benchmark functions, specifically f1 through f10. Drawing inspiration from the social behavior of elephant herds, the ‎EHO algorithm employs strategies that mimic the leadership of matriarchs, the exploration conducted by male elephants, and the dynamic ‎interplay between diversification and intensification during search. The study begins with a review of contemporary metaheuristic ‎algorithms used in optimization tasks—such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), ‎Simulated Annealing (SA), Firefly Algorithm (FFA), Cuckoo Search (CS), and Tree Physiology Optimization (TPO)—and highlights ‎existing performance gaps when solving complex benchmark functions. We then outline the design of the basic EHO method and introduce ‎improved variants that incorporate novel individual updating strategies. These improvements include replacing suboptimal individuals with ‎higher fitness particles, increasing the number of male elephants to enhance exploratory capabilities, and incorporating information from ‎previous iterations to accelerate convergence. The proposed algorithm is benchmarked on ten classic test functions to comprehensively ‎evaluate its convergence behavior, computational efficiency, and ability to search for global optima. Comparative analysis shows that the ‎improved variants of EHO not only achieve faster convergence but also demonstrate enhanced robustness in terms of statistical consistency ‎across multiple runs. The paper concludes with a discussion on the implications of these findings for both large-scale engineering ‎applications and further research on nature-inspired optimization techniques‎.

    Author Biography

    • Saman M. Almufti , Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Duhok, Iraq
      Swarm intellignce

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    Rajab Asaad, R., Ahmed Shaban, A., & Almufti , S. M. . (2025). Elephant Herding Optimization for Benchmark Problems: Algorithm ‎Design, Implementation, and Performance Analysis. International Journal of Scientific World, 11(2), 79-90. https://doi.org/10.14419/vc2c7252