Many-Objective Cheetah Optimizer: A Novel Paradigm for Solving Complex Engineering Problems

Complex many-objective optimization problems (MaOPs) generate multiple challenges for obtaining convergence alongside diversity within extensive multi-dimensional solution areas. Optimization approaches currently face limitations when trying to balance exploration and exploitation especially when resources become limited. MaOCO represents the Many-Objective Cheetah Optimization Algorithm which draws its concepts from the hunting behavior of cheetahs. MaOCO includes adaptive search functions that use attack and sit-and-wait approaches to optimize exploration and exploitation capabilities. MaOCO produces hypervolume (HV) results that exceed NSGA-III and MaOMVO by 50% while also delivering inverse generational distance (IGD) results which reach 40% better than both competing methods. The algorithm demonstrates superior efficiency in solving complex MaOPs, because it requires lower computational costs by 15%. MaOCO successfully traverses Pareto-optimal fronts according to theoretical evaluations, and its modular structure allows for both scale-up and hybridization features. The implemented applications of this approach include optimizing energy systems along with designing structures for engineering projects. Future researchers plan to integrate MaOCO with additional metaheuristic techniques to improve its performance when dealing with dynamic and irregular Pareto front problems.