【摘要】 蚁群算法是由意大利学者M.Dorigo等人提出,近几年迅速发展起来,并得到广泛应用的一种模拟进化的优化类算法。然而蚁群算法和其他进化算法一样存在搜索速度慢、易陷入局部最优的缺点。为了克服上述的不足,在MMAS基础上提出一种具有奖罚机制的分组蚁群算法,即在MMAS基础上对蚂蚁进行分组,利用蚂蚁组之间合作和组内蚂蚁相遇合作思想,并引入奖罚机制对信息素更新。实验数据表明改进后的算法避免了停滞陷入局部最优的现象且加快了搜索速度,最优解也较优。更多还原

【Abstract】 The Ant Colony Optimization(ACO) algorithm is a novel metaheuristic algorithm,which was proposed first by Italian scholars M.Dorigo,etc.,is developing fast in recent years and is put into more practical application to solve some practical and professional problem.But its searching speed is slow and it is easy to fall into the local best results as other evolutionary algorithm.In order to overcome the shortcoming,in this paper,A grouped ACO algorithm based on MMAS with award & penalty strategy is presented to improve the ACO algorithm,grouping the ants and taking advantage of the cooperation between and inside the grouped ants,renewing the pheromone with award & penalty strategy.The data of simulation of TSP problems show that it avoid stopping and falling to the local best results and its results and searching speed are better than the original ACO algorithm.更多还原