An Improved Ant Colony Optimization Algorithm Based on Pheromone Backtracking Liu， Zhiguo; Liu， Tao; Gao， Xiue; This paper appears in: Computational Science and Engineering (CSE)， 2011 IEEE 14th International Conference on Issue Date : 24-26 A 2011 On page(s): 658 - 661 Print ISBN: 978-1-4577-0974-6 Digital Object Identifier : 1109/CSE116 Date of Current Version : 01 十一月 2011 AbstractIn this paper， backtracking algorithm is adopted to the pheromone updating rule to resolve the basic Ant Colony Optimization (ACO) algorithm's shortcoming of easily falling into local When the pheromone accumulated to the backtracking point on the tour， pheromone will be backtracked in the improved The improved algorithm not only solves the ACO algorithm in excessive accumulation of pheromone problems， but also has better global search ability and convergence speed， which increase the quality of the solution space by using the information of the previous iterations' Finally， the improved algorithm is applied to the Traveling Salesman Problem(TSP)， and the simulation results show that it is much better than basic ACO algorithm in many aspects， such as the optimal iterations， the average and the optimal solution
