Application of an Improved Grey Wolf Optimization Algorithm in Path Planning

Within the domain of mobile robot path planning, an Enhanced Grey Wolf Optimal (IGWO) algorithm emerges to address the drawbacks present in the conventional Grey Wolf Optimal approach, such as sluggish convergence and limited global search capabilities. By introducing a nonlinear convergence factor and incorporating an inertia weight showcasing disturbance characteristics, enhancements bolster the algorithm's convergence speed and stability. Integration of the search mechanism from the Whale Optimization Algorithm and the introduction of a new Spiral Disturbance Search Strategy work together to amplify the algorithm's global search prowess. Eight sets of test functions show that IGWO has the advantages of strong global search ability and high optimization accuracy. The improved algorithm is implemented for mobile robot path planning in a 20x20 grid model with a static environment using Matlab. Algorithm programs based on GWO, GA and IGWO were employed to simulate robot obstacle avoidance and compute the optimal path. Advantages and disadvantages are compared through 10 independent tests. The testing results indicated that compared with GWO and GA, success rate of planning optimal path by IGWO is increased by 50% and 40%, respectively. It validates the improved Grey Wolf algorithm's superiority and feasibility in addressing the path planning challenge for mobile robots.