Investigating the Ability of Grey Wolf Optimizer to Solve Pressure Vessel Optimization Design Problem

This study investigates the effectiveness of the Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic algorithm, in solving the complex and constrained pressure vessel design optimization problem. The primary objective is to minimize the cost and weight of a pressure vessel while satisfying multiple nonlinear constraints related to material thickness, radius, and length. The GWO algorithm is benchmarked against several other well-known metaheuristic algorithms, including DE, GA, PSO, and CMA-ES, under identical experimental settings. To handle constraints, the death penalty method is applied. Results demonstrate that GWO performs competitively, ranking second overall in solution quality and convergence speed, surpassed only by Differential Evolution (DE). The findings highlight GWO’s potential as a robust and efficient optimization tool for solving real-world engineering design problems with complex search landscapes.