基于信息素初始分配和动态更新的蚁群算法

针对蚁群算法搜索初期收敛速度慢和容易陷入局部最优的问题,对蚁群算法进行改进.在初始化阶段,采用贪心策略构造次优路径并增加该路径上的信息素浓度,实现不同路径上信息素的初始分配,使信息素在搜索初期就能发挥指导性作用,让蚂蚁更快地趋向于最优解的附近;在迭代寻优过程中,引入遗传变异操作,对每次迭代后的最优路径作变异操作,尝试寻...

Pheromone Initialization and Dynamic Update Based Ant Colony Algorithm

Aiming at the problems of slow convergence speed and easy to fall into local optimum in the early stage of ant colony algorithm, ant colony algorithm is improved. In the initialization stage, the suboptimal path is constructed by the greedy strategy and the pheromone on it is increased to realize the initial distribution of pheromones on different paths, so that pheromones can play a guiding role in the early stage of search and make ants tend to the optimal solution faster. In the process of iterative optimization, genetic mutation operation is introduced to mutate the optimal path after each iteration and try to find a better path to adjust the pheromone increment adaptively. While the algorithm inevitably falls into local optimization, pheromone rollback strategy is executed to adjust the volatilization factor dynamically according to the number of rollback to enhance the search ability and make the algorithm easier to jump out of local optimum. The simulation experiments show that the improved algorithm can accelerate the convergence speed and enhance the ability to jump out of the local optimum effectively.