Research Of Ant Colony Algorithm & Its Application In Ad Hoc Network

Ad Hoc network is a special kind of wireless communication network. All nodes are equal, and there is no central control node in the network. Because of the mobility of nodes, network topology changes frequently and arbitrarily. This temporary autonomous system is very flexible, which has strong robustness and survivability. It shows good prospects in military area and civilian area. However, the routing protocols of traditional network can not be used due to the specificity of Ad Hoc network. In Ad Hoc network, the topology changes constantly, the wireless bandwidth is limited, the computing power and storage capacity of a node are very low, and the power consumption is also limited. All these reasons make how to design an effective routing protocol be an important subject in the development of Ad Hoc network.Ant colony algorithm is a heuristic search algorithm inspired by the foraging behavior of real ants. This algorithm uses autocatalytic positive feedback mechanism, has global optimization capability and parallelism in nature. It does not depend on the mathematical description of the specific issue. Ant colony algorithm shows outstanding performance and great potential in combinatorial optimization problems, so it is suitable for routing design of Ad Hoc network.For the frequent topology changes of Ad Hoc network and the inherent shortcomings of ant colony algorithm, a self-adaptive routing algorithm based on ant colony algorithm is proposed. The structure of routing table and pheromone updates are improved. The probabilistic selection and random selection are considered into ants'search strategy, and the ratio between them is adjusted adaptively to strengthen the initial global search capability and avoid falling into local optimum. Simulation results show that the algorithm performs well.To solve QoS multicast routing problem of Ad Hoc network, artificial immune algorithm is integrated with ant colony algorithm, considering the rapid random global search ability of artificial immune algorithm. Some better feasible solutions are found by using artificial immune algorithm, then initial pheromone distribution is generated to accelerate the convergence rate of ant colony algorithm. In process of looking for the optimal solution, the idea of antibody exclusion is used to avoid getting into local optimum. Simulation results show that the algorithm has a better ability to find excellent routings.