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为了提高传统蚁群优化算法求解的质量,对传统的蚁群优化算法进行了改进,引进了一种信息素适时交换方法,同时在信息素积累的过程中,自适应地改变信息素的挥发率,将算法中的正反馈作用抑制到适当的程度,扩大了可行解的范围,避免了算法过早的停滞,提高了解的质量,同时算法的收敛速度没有明显的降低.通过三种TSP问题的仿真实验,证明该算法具有较强的发现较好解的能力,解的稳定性也比较好.
In order to improve the quality of the traditional ant colony optimization algorithm, the traditional ant colony optimization algorithm is improved, and a pheromone exchange method is introduced in the meantime. In the process of pheromone accumulation, the pheromone volatility is adaptively changed , The positive feedback effect in the algorithm is suppressed to an appropriate level, the range of feasible solutions is expanded, the premature stagnation of the algorithm is avoided, the quality of the solution is improved, and the convergence speed of the algorithm is not obviously reduced.Through the three TSP problems Simulation experiments show that the algorithm has strong ability of finding better solutions and the stability of solution is better.