关键词: 灰狼优化算法, 信息熵, 邻域变异, 自适应变异

Abstract: Aiming at the problem that Automated Guided Vehicle (AGV) global path planning algorithm easy to fall into local minimum and slow convergence,the traditional Elitism Tactic was enhanced with Grey Wolf Optimization (GWO) to prevent population diversity worse gradually and improve its global search capability.To prevent genes on chromosomes to cluster around a small neighborhood,an Improved Genetic Algorithm (IGA) enhanced with adaptive mutation and crossover probability based on chromosome information entropy was proposed,in which a neighborhood mutation operator was adopted for gene fragments that intersected the obstacles to be avoided quickly.An AGV path planning platform for the proposed IGA was developed by using Matlab GUI tools.Experimental results showed that the proposed IGA and the developed platform could efficiently and reliably solve complex static environment AGV path planning problems.

Key words: grey wolf optimization algorithm, information entropy, neighborhood mutation, adaptive mutation