改进的蚁群算法求解旅行Agent问题

蚁群算法是优化领域中新出现的一种仿生进化算法，该算法具有并行、正反馈和启发式搜索等特点，但搜索时间长、易陷入局部最优解是其突出缺点。旅行Agent问题是一类复杂的组合优化问题，目的在于解决移动Agent 为完成用户指定任务，在不同主机间移动时的迁移策略问题。在蚁群算法的基础上，引入变异运算，并且对蚁群算法的全局和局部更新规则进行改进，引入自适应的信息素挥发系数来提高收敛速度和算法的全局最优解搜索能力，从而使得移动Agent在移动时以最优的效率和最短的时间来完成迁移。仿真结果表明，改进的算法在解的性能和收敛速度上均优于相关算法。

Traveling Agent problem based on improved ant colony algorithm

Ant colony algorithm is a novel category of bionic algorithm for optimization problems,which has the characteristic of parallelism,positive feedback and heuristic search,but it has the limitation of stagnation,and is easy to fall into local optimums. Traveling agent problem is a complex combinatorial optimization problem,which solves the problem of planning out an optimal migration path when agents migrate to several hosts.In this paper,an improved ant colony algorithm is presented.The local and global updating rules of pheromone are modified on the basis of ant colony algorithm,and a self-adaptive pheromone evaporation rate is proposed,which can accelerate the convergence rate and improve the ability of searching an optimum solution,so mobile agents can accomplish the migration task with high efficiency and short time.The results of contrastive experiments show that the algorithm is superior to other related methods both on the quality of solution and on the convergence rate.