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A modified ant colony algorithm to solve the shortest path problem

机译:一种改进的蚁群算法,用于解决最短路径问题

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To solve the problem that the ant colony algorithm is easy to fall into local optimal solutions in solving the shortest path problem, improvements on the classical ant colony algorithm are provided in three aspects. Firstly, direction guiding is utilized in the initial pheromone concentration to speed up the initial convergence; secondly, the idea of pheromone redistribution is added to the pheromone partial renewal process in order to prevent the optimal path pheromone concentration from being over-damped by the path pheromone decay process; finally, a dynamic factor is invited to the global renewal process to adaptively update the pheromone concentration on the optimal path, in which way the global searching ability is improved. The results of the simulation experiment show that this modified algorithm can greatly increase the probability of finding the optimal path while guaranteeing the convergence speed.
机译:为了解决蚁群算法在解决最短路径问题时容易陷入局部最优解的问题,对经典蚁群算法进行了三个方面的改进。首先,在初始信息素浓度中利用方向引导来加速初始收敛。其次,在信息素部分更新过程中增加了信息素重新分配的思想,以防止最佳的路径信息素浓度被路径信息素的衰减过程过度抑制。最后,在全局更新过程中引入动态因素,以自适应地更新最佳路径上的信息素浓度,从而提高了全局搜索能力。仿真实验结果表明,该改进算法在保证收敛速度的同时,可以大大提高寻找最优路径的概率。

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