基于邻域搜索的改进最大最小蚁群算法

The Improved Max Min Ant Colony Algorithm Based on Neighborhood Search

针对蚁群算法求解旅行商问题时易陷入局部最优的问题,提出一个改进的混合最大最小蚁群算法,并应用于求解旅行商问题。上述算法设计了一种新的信息素更新模型,单个蚂蚁每走一步就进行信息素局部更新,在所有的蚂蚁搜索一周后,最优路径蚂蚁进行全局信息素更新。提出一种新的邻域搜索模型,将邻域大小设置为原来的一半,提高了计算的效率。在每个蚂蚁的一个周期循环后,使用邻域搜索算法优化最优解的路径长度。仿真结果表明,改进算法具有较高的求解精度和收敛速度。

Aiming at the Traveling Salesman Problem based on ant colony optimization which is easy to fall into local optimums, this paper proposes an improved mixed max rain ant colony algorithm, and it is applied to solve the traveling salesman problem. The algorithm designs a new pheromone updating model, an single ant of moving every step will update Local pheromone, after all the ants travel the week, the ant of optimal path updates global pheromone. In order to improve the computational efficiency, the article puts forward a new model of neighborhood search and the neighborhood size is set to half of the original. After a cycle of each ant, this article uses neighborhood search algo- rithm to optimize the use of the path length of the optimal solution. The simulation results show that the improved algo- rithm has higher accuracy and convergence rate than the classical ant colony algorithm.