关键词: 人工智能, 元启发式算法, 灰狼优化算法, 路径规划

Abstract: Aiming at  the problem of uneven initial solution distribution in the traditional gray wolf optimization algorithm, we proposed an enhanced gray wolf optimization (EGWO) algorithm. Firstly, we introduced nonlinear convergence factors to improve gray wolf optimizaiton algorithm. Secondly,  the Sobel sequence was integrated into the improved gray wolf optimization algorithm to increase the population diversity. In order to verify the effectiveness of the proposed algorithm, EGWO algorithm was applied to UAV path planning, and compared with the traditional gray wolf optimization algorithm based on multiple evaluation indicators. Experimental results show that the EGWO algorithm has better performance, and  can quickly and accurately plan and control the flight path of UAVs in complex environments, as well as improve the flight efficiency of UAVs in swarm control.

Key words: artificial intelligence, meta-heuristic algorithm, gray wolf optimization algorithm, path planning