摘要
蚁群算法作为解决TSP中组合优化问题方案,其搜索路径能力较其它算法优异,但传统蚁群算法的选取策略较随机,导致进化速度慢。为了优化传统蚁群算法速度较慢、过早收敛以致停滞现象,针对概率选取公式随机搜索下一节点,以延缓其收敛速度。对信息素调节公式进行更新以提高蚁群的搜索能力。实验结果表明,改进算法在最短路径、平均路径和搜索最短路径时间上较蚁群算法提高很大,改进的蚁群算法能有效提高算法的收敛速度和搜索能力。
The ant colony algorithm in solving TSP as a combinatorial optimization problem,its ability to search path than other algorithms in terms of a better,but the traditional ant colony algorithm is a random selection of strategy,lead to evolution slower.In order to optimize the speed of the traditional ant colony algorithm,the speed of the algorithm has been slow to stop.According to the probability selection formula,we randomly searched the next city,so we reintroduced the formula to delay its convergence rate.Secondly,the adjustment formula of pheromone is updated to improve the search ability of ant colony.Experiments show that improved algorithm in the shortest path,the average shortest path and the search path time than ant colony algorithm is improved greatly,which confirmed that the proposed algorithm can effectively improve the convergence speed and search ability of the algorithm.
出处
《软件导刊》
2018年第2期56-59,共4页
Software Guide
关键词
蚁群算法
正反馈
信息素
收敛速度
TSP问题
ant colony algorithm
positive feedback
pheromone
speed of convergence
the TSP problem
