The grey wolf optimization algorithm is a metaheuristic optimization algorithm based on the behavior of grey wolf groups in nature, which has the advantages of a simple concept and few adjustment parameters and is widely used in various fields. But the grey wolf optimization algorithm has disadvantages such as being prone to getting stuck in local optima, low convergence efficiency, and poor robustness.To address the above shortcomings, this study proposes an improved grey wolf optimization algorithm, which draws on the gold migration formula in the gold mining optimization algorithm and introduces chaotic mapping, the gold mining optimization algorithm, the vertical and horizontal crossover strategy, and the Gaussian mutation. Chaos mapping is used to initialize the grey wolf population so that the population is more evenly distributed in the search space; the α-wolf of the grey wolf algorithm is updated by using the gold migration formula in the gold mining optimization algorithm to increase the diversity of the algorithm; searching is carried out by horizontal crossover, which reduces the blind zone of the search to make the algorithm have a better global search capability; vertical crossover prevents the algorithm from converging prematurely; and the introduction of the Gaussian mutation effectively prevents the algorithm from falling into the local optimum premature problem. In order to verify the effectiveness of the algorithm, this study compares the improved Grey Wolf optimization algorithm with other Grey Wolf optimization algorithms on 23 benchmark functions. After experimental verification, the proposed algorithm is better than the other comparative algorithms. Meanwhile, when the algorithm is applied to path planning, the paths obtained are shorter and the running time is shorter than that of other algorithms, which further verifies the applicability of the algorithm.
Cheng et al. (Mon,) studied this question.