مشخصات پژوهش

Navigating complex engineering systems, with their dense networks of dependent components, is a challenging task. It often requires significant computing power to model their complexity [1]. Traditional optimization methods that rely on gradients often fall short. They tend to get stuck in suboptimal local solutions and require a lot of computation. On the other hand, meta-heuristic algorithms have become effective and adaptable options. They can efficiently explore large and complicated search spaces. These methods draw inspiration from various natural phenomena, such as evolution and group animal behavior. A key principle in this area is the "No Free Lunch" theorem [2], which states that no single algorithm is the best for every problem . A method that works well for one type of problem might not work well for another. This fact highlights the ongoing need for innovation and the creation of new solvers to keep up with the changing demands of complex optimization challenges. The increasing complexity of real-world optimization problems has led to a strong interest in hybrid metaheuristics. These approaches combine the strengths of various algorithms to address their individual weaknesses and improve performance. By merging the global exploration ability of one method with the local exploitation skill of another, these hybrids develop a more effective and efficient search strategy. This often results in better solutions and quicker convergence compared to using any single algorithm on its own. Here are five prominent hybrid algorithms: (1) Memetic Algorithm (MA): This is a hybrid of a Genetic Algorithm (GA) with a local search technique [3]. The GA performs a broad exploration of the search space, while the local search (e.g., Hill-Climbing) refines individual solutions, allowing for intensive exploitation of promising regions. (2) Grey Wolf Optimizer - Simulated Annealing (GWO-SA): This hybrid combines the Grey Wolf Optimizer (GWO), which is effective for explorati