Application of an Ant Colony Algorithm based on complex networks in migration of mobile agents

One of the main problems in mobile agent migration is planning out an optimal migration path according to the agent tasks and other restrictions when agents migrate to several other hosts. The Ant Colony Algorithm, which has the characteristic of parallelism, positive feedback and heuristic search, is a new evolutionary algorithm and is extremely suitable to the mobile agent migration problem. But it still has some shortcomings such as slowly speed and stagnation behavior. Complex networks theory is a new kind of theory, which finds that some practical networks have new characters. In order to describe these new characters, some new characteristic measures are introduced, one of which is the node's “degree”. Based on the classical Ant Algorithm, the parameter “degree” is added into the state transfer rules of the Ant 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. This improved Ant Colony Algorithm is used to plan out an optimal migration path of mobile agents. 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.