مشخصات پژوهش

Managing complex engineering schematics filled with interdependent components requires significant computational power to handle the details. Traditional gradient-based techniques often get stuck at mediocre local solutions, wasting resources in the process [1]. On the other hand, meta-heuristic strategies have become dependable tools for exploring large and complicated solution spaces. Their strength comes from biomimicry, which mimics natural processes like insect swarms or evolutionary selection and incorporates these ideas into computational methods. Examining optimization theory leads to the "No Free Lunch" theorem: no single algorithm excels in all situations since each has trade-offs in performance [2]. Therefore, solving difficult optimization problems will continually drive innovation in algorithm design, evolving alongside the challenges. Hybridizing diverse meta-heuristic algorithms has emerged as a powerful strategy to boost optimization performance, leveraging the complementary strengths of individual methods to overcome their isolated limitations [3]. By integrating mechanisms such as genetic evolution with swarm intelligence or simulated annealing with ant colony behaviors, these ensemble approaches achieve greater exploration-exploitation balance, escape local optima more effectively, and deliver superior solutions across complex, multimodal search spaces—often with faster convergence and enhanced robustness compared to standalone techniques. Here are four examples of hybrid metaheuristic algorithms: (1) Honey Badger Algorithm and Sand Cat Swarm Optimization (HBA-SCSO) [4]. (2) Particle Swarm Optimization and Generalized Normal Distribution Optimization (PSO-GNDO) [5]. (3) Whale Optimization Algorithm and Tiki-Taka Algorithm (HWOA-TTA) [6]. (4) Bald Eagle Search optimization algorithm with dynamic opposition‐based learning (SABES) [7].