Bio-Inspired Metaheuristic Algorithms for High-Dimensional Engineering Optimization: Theory and Computational Analysis
DOI:
https://doi.org/10.63503/j.ijcma.2025.196Keywords:
Engineering Optimization, Metaheuristics, Bio-Inspired Algorithms, High-Dimensional Problems, Particle Swarm Optimization, Genetic Algorithm, Grey Wolf Optimizer, Global Search, Computational AnalysisAbstract
The pursuit of the best design in the world of engineering is not a fixed dilemma, but a clean-up fight against complexity. Engineering systems are becoming bigger and more complex, and the scale of its design space is exploding, making a conventional optimization method ineffective. As such it needs the construction of solvers capable of wandering through this wilderness of high dimensionality in real time, not the fixed, deterministic, models. The present paper is a computational framework of high-dimensional optimization in engineering directly which tackles the challenge of the curse of dimensionality. We do not view the complex set of CEC 2022 benchmark visions as a system of equations but as an approximation of the multi-modality and rugged terrain of engineering challenges in real life. We use a COP that focuses on a strict comparison of three bio-inspired metaheuristics, namely Particle Swarm Optimization, the Genetic Algorithm, and the Grey Wolf Optimizer, which are population-based algorithms, which can search huge search space without gradient information. We demonstrate through exhaustive statistical analysis that although each algorithm has its own strength, the two-level confidence is that the Grey Wolf Optimizer has a better convergence speed and stability and can be able to recover and adjust its search strategy to act in more complex functional landscapes. The work constitutes the initial excellence of bio-inspired metaheuristics to the complicated optimization implementations and creates a foundational roadmap that is practical to adhere to in choosing solvers, which learn and grow instead of merely performing a search exercise that is preset and programmed to act in a predetermined manner.
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