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中文摘要：
遗传算法具有快速随机的全局搜索能力,但对于系统中反馈信息利用却无能为力,求精确解效率低.改进型ACS（antcolony system）算法不仅具有分布式并行全局搜索能力,而且在很大程度上避免了候选解陷入局部极小并导致系统收敛到这一伪最优解从而停止进化的可能性,但存在初期信息匮乏,求解速度慢的缺点.为了改善移动Agent系统的迁移性能和执行效率,本文提出一种基于由遗传算法和改进型ACS算法组成的混合智能算法的移动Agent路由算法.该路由算法是汲取两种智能算法的优点,克服各自的缺陷.通过对TAP问题的仿真实验表明该算法取得了较好的效果.
英文摘要：
Genetic algorithm has the ability of doing a global searching quickly and stochastically.But it cann＇t make use of enough system output information,and the efficiency to solve precision results is reduced.The enhanced ant colony system（ACS）algorithm not only has the ability of parallel processing and global searching,it but also can avoid the possibility of stopping evolution for the convergence of the system to a pseudo-optimization solution for the fact that the candidate solution reach the partial infinitesimal.But the speed at which the ant algorithm gives the solution is slow,because there is little information pheromone on the path early.In order to improve the migration performance and the execution efficiency of mobile agent systems,an itinerary algorithm for mobile agents based on a combined intelligent algorithm composed of genetic algorithm and the enhanced ACS algorithm is provided in this paper.This algorithm proposed takes advantage of merits of the two algorithms,avoiding the shortcomings of each.The simulation results for Traveling Agent Problem（TAP）show that very nice effects are obtained.The migration performance and execution efficiency of mobile agent systems is decided directly by the efficiency of the itinerary algorithm for mobile agents.
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