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Abstract
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Handling complex engineering blueprints with lots of interlocking parts is a real challenge that needs a lot of computational oomph to get the intricacies under control [1]. Conventional methods that rely on gradients to find the perfect solution often hit a brick wall, getting stuck in sub par local optimums while using up way too much processing muscle. But on the other hand , meta-heuristic approaches have come a long way, and are now a solid way to navigate big, complicated search spaces. Part of what makes them so effective is seeing how things work in nature - stuff like animal colonies, and how evolution adapts over time - and then translating those ideas into algorithms [2]. When you start to dig into the theory of optimisation, one thing that becomes clear is the "No Free Lunch" theorem, which basically says that no one algorithm is going to be a winner across the board - every approach has its own strengths and weaknesses. That means that when it comes to solving tricky optimisation problems, researchers are going to keep coming up with new ideas and new ways of doing things. And that's a challenge that will keep on evolving alongside the problems we're trying to solve.
Combining different metaheuristic algorithms has become a popular way to improve optimization results. Each algorithm has unique strengths and weaknesses, and their integration can create more powerful and efficient problem-solving methods. Metaheuristic algorithms, such as Genetic Algorithm (GA) [3] and Particle Swarm Optimization (PSO) [4], are widely used to find near-optimal solutions in complex, nonlinear, or high-dimensional problems. However, a single algorithm may struggle with slow convergence or may get trapped in local optima. By combining two or more metaheuristics, researchers can improve exploration and exploitation capabilities. This leads to faster convergence, higher accuracy, and greater robustness in finding optimal solutions across various applications in engineering, da
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