Grey Wolf Algorithm with Rat Swarm Optimizer for Constrained Optimization and Engineering Design Problems

This paper proposes a new hybrid optimization algorithm that combines the grey wolf optimization (GWO) algorithm with rat swarm optimizer (RSO) to improve the exploration and exploitation capabilities of the search space. The proposed algorithm, named GRO, is evaluated using benchmark functions and engineering design problems. The GRO algorithm leverages the social hierarchy and hunting strategies of wolf packs and incorporates the swarm intelligence of rats and the diversity-enhancing effects of GWO to guide the search process. Experimental results demonstrate that the GRO algorithm outperforms existing state-of-the-art optimization algorithms in terms of convergence speed, accuracy, and robustness for most benchmark functions. The hybrid algorithm’s ability to balance exploration and exploitation is shown to be superior to other metaheuristic algorithms. The algorithm is further validated by solving two well-known engineering design problems, where it demonstrates its efficacy in solving practical constrained problems.