An Improved Gray Wolf Optimization Algorithm to Solve Engineering Problems

With the rapid development of economy, disparity between supply and demand resources is becoming increasingly prominent in engineering design. In this paper, an improved gray wolf optimization algorithm proposed (IGWO) to optimize design problems. First, a tent map used generate initial location population, which evenly distributes population lays foundation for diversified global search process. Second, Gaussian mutation perturbation perform various operations on current optimal solution avoid falling into local optima. Finally, cosine control factor introduced balance exploration capabilities improve convergence speed. The IGWO applied four problems with different typical complexity, including pressure vessel design, tension spring welding beam three-truss experimental results show that superior other comparison algorithms terms performance, stability, applicability effectiveness; can better solve problem resource waste also optimizes 23 types function uses Wilcoxon rank-sum test Friedman verify has higher speed, precision robustness compared algorithms.