Hybrid Optimization Algorithm for solving Path Planning Problems Based on Grey Wolf Optimization Algorithm

Abstract

The grey wolf optimization algorithm is a heuristic optimization algorithm based on the behavior of grey wolf groups in nature. It has the advantages of a simple concept and few adjustment parameters, and it is widely used in a variety of fields. To address the above shortcomings, this study proposes an improved grey wolf optimization algorithm that uses the gold migration formula from the gold mining optimization algorithm and incorporates 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, ensuring that it is more evenly distributed across the search space. The grey wolf algorithm's α-wolf is updated with the gold migration formula from the gold mining optimization algorithm, increasing its diversity. Horizontal crossover is used for searching, which reduces the algorithm's blind zone and improves its global search capability. Vertical crossover prevents the algorithm from converging prematurely. The introduction of the Gaussian mutation effectively prevents the algorithm from falling into the local optimum premature problem. To determine the algorithm's effectiveness, this study compares the improved Grey Wolf optimization algorithm to other Grey Wolf optimization algorithms on 23 benchmark functions. After experimental verification, the proposed algorithm outperforms the other comparative algorithms. Meanwhile, when the algorithm is applied to path planning, the paths generated are shorter, and the running time is shorter than that of other algorithms, demonstrating the algorithm's applicability.