Comparative Performance Analysis of Metaheuristic Algorithms for Constrained Engineering Design Optimization

Optimization is essential today to enhance performance outputs, faster speeds, and greater performance by improving systems, processes, and algorithms. In industries like manufacturing, engineering, and devices, this is essential to improve efficiency and performance in metaheuristic techniques for organizing, navigation, resource allocation, creation of networks, energy management, economics, logistics, artificial intelligence, the medical field, and e-commerce. These algorithms leverage exploitation and exploration to achieve globally optimal or near-optimal results. This article discusses the experimental analysis of nature-inspired optimization algorithms, namely Meerkat Optimization Algorithm (MOA), Walrus Optimizer (WO), and Frilled Lizard Optimization (FLO). The research study indicates WOA generated higher improvements over the best values rankings, having 1.6953 at welded beam design, at compression spring design 3.6618, and pressure vessel design having 2302.5, proving its correctness and consistency. An experimental study is conducted in this work to demonstrate how these algorithms can be efficient techniques for solving complex real-world problems and increasing optimization efficiency. This work presents a unique empirical comparison of three relatively recent metaheuristic algorithms (MOA, FLO, and WOA) on real-world constrained engineering problems, which has not been comprehensively explored in earlier literature.