WHOFWA: An effective hybrid metaheuristic algorithm based on wild horse optimizer and fireworks algorithm

kground and Objectives: This paper explores the realm of optimization by synergistically integrating two unique metaheuristic algorithms: the Wild Horse Optimizer (WHO) and the Fireworks Algorithm (FWA). WHO, inspired by the behaviors of wild horses, demonstrates proficiency in global exploration, while FWA emulates the dynamic behavior of fireworks, thereby enhancing local exploitation. The goal is to harness the complementary strengths of these algorithms, achieving a harmonious balance between exploration and exploitation to enhance overall optimization performance.Methods: The study introduces a novel hybrid metaheuristic algorithm, WHOFWA, detailing its design and implementation. Emphasis is placed on the algorithm's ability to balance exploration and exploitation. Extensive experiments, featuring a diverse set of benchmark optimization problems, including general test functions and those from CEC ۲۰۰۵, CEC ۲۰۱۹, and ۲۰۲۲, assess WHOFWA's effectiveness. Comparative analyses involve WHO, FWA, and other metaheuristic algorithms such as Reptile Search Algorithm (RSA), Prairie Dog Optimization (PDO), Fick’s Law Optimization (FLA), and Ladybug Beetle Optimization (LBO).Results: According to the Friedman and Wilcoxon signed-rank tests, for all selected test functions, WHOFWA outperforms WHO, FWA, RSA, PDO, FLA, and LBO by ۴۲%, ۵۵%, ۷۴%, ۷۱%, ۴۸%, and ۵۲%, respectively. Finally, the results derived from addressing real-world constrained optimization problems using the proposed algorithm demonstrate its superior performance when compared to several well-regarded algorithms documented in the literature.Conclusion: In conclusion, WHOFWA, the hybrid metaheuristic algorithm uniting WHO and FWA, emerges as a powerful optimization tool. Its unique ability to balance exploration and exploitation yields superior performance compared to WHO, FWA, and benchmark algorithms. The study underscores WHOFWA's potential in tackling complex optimization problems, making a valuable contribution to the realm of metaheuristic algorithms.

کلیدواژه ها:

Wild horse optimizer ، fireworks algorithm ، convergence speed ، hit rate