Research On Migration Technology Of Mobile Agent

With the development of Internet, network technology and distributed artificial intelligence field have continuously achieved new breakthrough. The traditional distributed computing model can not meet the demands of complex distributed computing on heterogeneous network. Based on theses studies, mobile agent technology which is a new network computing technology is proposed. Mobile agent has the character of autonomy, collaboration and mobility. Mobile agent can independently move in heterogeneous network and seek the appropriate computation resources. It will migrate from machine to machine and accomplish the specific task on behalf of the user. The model is fit for the demand of distributed computing. Mobile agent technology provides new thoughts and methods for the information organization, information high efficiency access and information sharing in distributed computing environment. The computing model of mobile agent can overcome the disadvantages of traditional information management and information sharing and improve the information sharing and acquisition capacity in distributed computing environment. The remarkable characteristic of mobile agent is mobility. Reasonable routing policy and routing planning will obviously improve the system performance of mobile agent.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. A mutation operator is introduced and the local and global updating rules of pheromone are modified on the basis of ant colony algorithm. The algorithm greatly decreases the possibility of halting the ant system due to arriving at local minimum. The simulation experiment results show that mobile agent can accomplish the computing task with high efficiency and short time.Aiming at genetic algorithm and particle swarm optimization has lower searching efficiency in solving route choice of mobile agent. The key idea behind cultural algorithm is to explicitly acquire problem-solving knowledge from the evolving population and in return apply that knowledge to guide the search. In this paper, routing problem of mobile agent is formally demonstrated; also solving model of Multi-Constrained non-dominated optimal route is presented. Cultural algorithm is designed to solve the problem of mobile agent's routing: accepting the best individuals to improve the evolution of belief space by simulated annealing, search step length as situational knowledge is used to guide searching of the optimal solution in population space. The simulation experiment shows that the algorithm produces highly competitive results at a relatively low computational cost.Aiming at the existed mobile agent migration strategy can not dynamically choose migration host for mobile agent. Based on the existing model and criterion of mobile agent, machine learning theory is combined with the correlation theory of support vector machine and the model of mobile agent is improved. Intelligent migration strategy and model of mobile agent based on support vector machine are proposed. Mobile agent with support vector machine can perceive the changes of environment, react immediately, embodying the reactivity and autonomy of agent. Compared with other migration strategies, it can obtain the optimal result with high probability. The simulation experiment results on Aglet platform show that the migration strategy is effective and available.Migration security is one of main security problem of mobile agent. In this paper, we analyze the existing effective migration protocol and point the protocol has serious security hidden trouble:it is not against collusion of malicious hosts. Based on this protocol, using hash function, a security itinerary protection of mobile agent based on Merkle trees is proposed. Security and computational complexity are discussed in detail. According to the existing protocol, improved protocol meets the demand of security. Computational complexity is reduced.