关键词: 灰狼优化算法, 路径微调算子, 邻域变异算子, 遗传算法, 初始解生成算法

Abstract: Aiming at the inefficiency and premature of intelligent optimization algorithms solving the path planning problem of Automated Guided Vehicle(AGV),an improved Grey Wolf Optimization (GWO) algorithm was proposed for solving AGV path planning problems in complex environment,in which path fine-tuning operator and neighborhood mutation operator were introduced to improve the exploitation capability of GWO.A new initialization algorithm was further introduced to improve the quality of initial population,and an improved path segment and obstacle intersection judgment approach was employed to enhance the improved GWO.A new obstacle avoidance operator was proposed to improve the efficiency of path segments avoiding obstacles,and an AGV path planning prototype platform with multiple intelligent algorithms was developed by MATLAB GUI tools to verify the improved GWO compared with traditional genetic algorithms with single and multi-population strategy under different complex static environments.

Key words: grey wolf optimization algorithm, path fine-tuning operator, neighborhood mutation operator, genetic algorithms, initialization method