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Working with complicated engineering designs that have many parts that interact with each other is a challenging job. It often needs a lot of computer power to handle all the details [1]. Traditional optimization methods that rely on gradient calculations often run into problems. They can get stuck in local optima and require a lot of computing power. On the other hand, meta-heuristic algorithms have become strong and flexible tools that can effectively search through large and complex areas. These algorithms are based on different natural events, the way animals work together, and the processes of evolution. Some popular examples are Ant Colony Optimization (ACO) [2], Genetic Algorithm (GA) [3], Particle Swarm Optimization (PSO) [4], and Parrot Optimizer (PO) [5]. A key idea in optimization theory is the "No Free Lunch" theorem. It states that there is no one algorithm that can be the best for every type of problem. In simple terms, a method that works really well for one kind of problem might not work at all for a different kind. This situation highlights the ongoing need for new ideas and the creation of fresh problem-solving techniques to address the constantly changing challenges that come with complex optimization tasks. Combining different metaheuristic algorithms has become a popular way to improve optimization results. This method takes advantage of the strengths of each algorithm while also making up for their weaknesses. This combination usually results in quicker progress, more accurate solutions, and a better balance between exploring new options and using what is already known. Hybrid methods combine the strengths of different algorithms to solve problems more effectively. They can take the broad search abilities of one algorithm and the detailed improvement techniques of another. This way, they can mix approaches that focus on groups of solutions with those that follow a single path, allowing them to adapt well to different challenges in optimization.