Complex Environment Path Planning Based on an Improved Ant Colony Algorithm
This paper proposes an improved ant colony algorithm to solve the problem of slow and poor convergence.First,a correction strategy is introduced,which includes two local correction methods to reduce invalid paths.Second,an adaptive pheromone updating mechanism is devel-oped to distinguish and volatilize the initial pheromone from the pheromone released.For the pheromone released in each iteration,a change law of time-varying volatilization factor is de-signed to volatilize independently and obtain pheromone volatilization mechanism with adaptive volatilization intensity.Finally,the proposed algorithm is applied to mobile robot path planning.Compared with the existing improved ant colony algorithms,the results show that the improved algorithm is excellent in terms of effective time,average distance and shortest distance.
ant colony algorithmimproved ant colony algorithmglobal optimizationpath planning
万方数据
维普
