Migration Strategy for Mobile Agent Based on Distributed Genetic Algorithm
基于分布式遗传算法的移动Agent迁移策略

Migration strategy is one of the most critical problems in the system based on mobile Agent, the efficiency of mobile Agent depends on the optimal migration strategy. This paper proposes a new genetic Algorithm, called Extended Distributed Genetic Algorithms (EDGA, for multiple constrained migration strategy of mobile Agent. EDGA combines the advantages of Distributed Genetic Algorithms with Cascade Model. We design a central monitor dynamically allotting the size of sub-populations according to their performance, directing the migration, therefore making the searching of routes converge to the global optimization faster and better. The result of the experiments shows that the method is more effective than others.