مشخصات پژوهش

Dealing with complex engineering designs that involve many interconnected variables often requires finding the most effective solutions. This task demands a lot of computational resources. Traditional gradient-based optimization techniques often struggle in these situations. They can get stuck in local minima and usually require high computational costs [1]. On the other hand, meta-heuristic algorithms provide a flexible and efficient alternative. They can explore large search spaces to find near-optimal solutions. These algorithms often take inspiration from natural events, group animal behaviors, and evolutionary processes. This leads to popular methods like Genetic Algorithm (GA) [2], Particle Swarm Optimization (PSO) [3], Ant Colony Optimization (ACO) [4]. According to the “No Free Lunch” theorem in optimization theory, no single algorithm consistently performs better than all others across different problem areas [5]. An approach that works well in one situation might not succeed in another. This highlights the ongoing need to innovate and develop new meta-heuristic methods to address the changing challenges in optimization problems. Hybridizing meta-heuristic algorithms means combining two or more optimization methods to take advantage of their strengths and address their weaknesses [6]. The main goal of hybridization is to improve performance in terms of convergence speed, solution accuracy, and robustness. For instance, one algorithm might excel at global searching to explore the solution space well, while another might concentrate on local refinement to fine-tune promising solutions. By bringing together these strategies, hybrid approaches can balance exploration and exploitation more effectively than standalone methods. Common hybridization frameworks include combining Genetic Algorithms with Particle Swarm Optimization or integrating Ant Colony Optimization with local search techniques. These hybrid meta-heuristic systems have shown to be very effective f