Abstract
With the rapid development of the economy, the disparity between supply and demand of resources is becoming increasingly prominent in engineering design. In this paper, an improved gray wolf optimization algorithm is proposed (IGWO) to optimize engineering design problems. First, a tent map is used to generate the initial location of the gray wolf population, which evenly distributes the gray wolf population and lays the foundation for a diversified global search process. Second, Gaussian mutation perturbation is used to perform various operations on the current optimal solution to avoid the algorithm falling into local optima. Finally, a cosine control factor is introduced to balance the global and local exploration capabilities of the algorithm and to improve the convergence speed. The IGWO algorithm is applied to four engineering optimization problems with different typical complexity, including a pressure vessel design, a tension spring design, a welding beam design and a three-truss design. The experimental results show that the IGWO algorithm is superior to other comparison algorithms in terms of optimal performance, solution stability, applicability and effectiveness; and can better solve the problem of resource waste in engineering design. The IGWO also optimizes 23 different types of function problems and uses Wilcoxon rank-sum test and Friedman test to verify the 23 test problems. The results show that the IGWO algorithm has higher convergence speed, convergence precision and robustness compared with other algorithms.
Highlights
Many countries see the importance of resource optimization and sustainable development and direct research to save resources and maintain sustainable development
The results show that the improved optimization algorithm of gray wolf (IGWO) algorithm has higher convergence speed, convergence precision and robustness compared with other algorithms
To further evaluate the performance of the IGWO, the IGWO algorithm was tested on 23 benchmark functions, and the results were compared with the Gray wolf optimization (GWO), sine–cosine algorithm [6] (SCA), moth–flame optimization algorithm [4] (MFO), particle swarm optimization [3] (PSO), bat algorithm [7] (BA), flower pollination algorithm [8] (FPA) and SSA 7 algorithms
Summary
Many countries see the importance of resource optimization and sustainable development and direct research to save resources and maintain sustainable development. Hi Jun T et al proposed a GWO algorithm combined with a PSO algorithm, which retains the optimal position information of individuals and avoids the algorithm falling into local optima This algorithm is used to test 18 benchmark functions and has better results than other algorithms [25]. Zhang et al added elite opposition-based learning and a simple method to solve the problem of poor population diversity and slow convergence speed of the GWO, which increased the population diversity of gray wolf and improved the exploration ability of gray wolf [26]. The tent chaos is used to increase the population diversity, the Gaussian perturbation is used to expand the search range, and the cosine control factor is used to balance the global exploration and local development ability so as to avoid the basic gray wolf algorithm falling into the local optima and improve the solution accuracy. The results show that the IGWO algorithm has higher convergence speed, convergence precision and robustness compared with other algorithms
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