摘要
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 earlier stagnation in the conventional ant colony optimization, which easily leads to local optimal solution, an improved algorithm was proposed. In the algorithm, a new mechanism of trail information exchange between edges was introduced; on the other hand, the trail information volatilization was modified adaptively with the algorithm operating. By those, the function of positive feedback in ACO was suppressed to a reasonably degree so that the algorithm will not stopped earlier, the area of feasible solutions was expanded, and hence, a better solution can likely be got, at the same time the convergence speed was not reduced distinctly. Experimental results on three TSPs show that the algorithm has more powerful capacity of finding global solution and stability than that of conventional ant colony optimization.
出处
《系统工程理论与实践》
EI
CSCD
北大核心
2006年第5期93-98,共6页
Systems Engineering-Theory & Practice
关键词
蚁群算法
正反馈
优化
旅行商问题
ant colony algorithm
positive feedback
optimization
traveling salesman problem
