蚁群算法在移动Agent迁移中的应用研究

移动Agent提供了一种全新的分布计算范型 .移动Agent技术给分布式系统的设计、实现和维护都带来了新的活力 .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在不同主机间移动时如何根据移动Agent的任务和其他约束条件来规划最优的迁移路线 .蚁群算法作为一种新的生物进化算法,具有并行、正反馈和启发式搜索等特点,是一种解决旅行Agent问题的有效手段,受到了广泛的关注,但它与其他进化算法一样存在易陷入局部最小的缺点 .在蚁群算法的基础上,通过修改它的信息素轨迹更新规则,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 .

Application of an Ant Colony Algorithm in Migration of Mobile Agent

Mobile agent provides a novel paradigm for distributed computing. It has the potential to offer a single, general framework in which a wide range of distributed systems can be implemented efficiently, easily and robustly. The traveling agent problem is a complex combinatorial optimization problem, which solves the problem of planning out an optimal migration path according to the tasks and other restrictions when agents migrate to several hosts. Ant colony algorithm is a new evolutionary algorithm and extremely suit to solve the travelling agent problem, which has the characteristic of parallelism, positive feedback and heuristic search. To avoid the limitation of ant colony algorithm such as stagnation like other evolutionary algorithm, an improved ant colony algorithm is introduced to solve the travelling agent problem by modifying pheromone updating strategy, 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.