Optimization of Speed Reducer Design based on an Enhanced Grey Wolf Optimizer

Abstract

Traditional swarm intelligence optimization methods perform erratically in engineering design due to difficulties in handling nonlinear data, local optimal errors and premature convergence. To address these problems, we developed an enhanced Gray Wolf Optimizer (OGWO) that employs Levy flight and elite adversarial-based learning methods. We evaluated its effectiveness using 20 benchmark functions and compared it with other GWO variants and popular algorithms. The results show that OGWO is superior in terms of convergence speed, accuracy, and freedom from stagnation, as confirmed by the Wilcoxon rank sum test. Furthermore, the effectiveness of OGWO in training Multilayer Perceptron (MLP) has been evaluated using the UCL datasets. Finally, OGWO has been applied to solve the gearbox design problem, proving its ability to provide optimal solutions in addressing real-life engineering issues.