Study Of Multi-constrained QoS Routing Based On Adaptive ACO

With the rapid development of network technology, multimedia business emerge as the times require. The traditional Internet offer do everything in one's power service mode has been unable to meet the needs of users, on the network quality of service, QoS(Quality of Service, QoS) requirements more stringent, such as delay, bandwidth, packet loss rate of cyber source and have certain requirements. Many existing algorithms only for one or two constraint condition is generated(such as the minimum bandwidth or the hour delay), in a variety of QoS constraints, these algorithms have some limitations. How to solve the multi constrained QoS routing problem, how to meet the requirement of the business at the same time, as far as possible to reduce the consumption of resources, the rational allocation of the network traffic load, reduce the blocking rate, become the focus of attention.This paper discusses the multi constrained QoS Routing Research on adaptive ant colony algorithm and adaptive quantum based on ant colony algorithm, the main research work are as follows:Firstly, In order to solve the problem of QoS routing in the search of the best link to meet the problem of delay, jitter, and many other constraints of energy performance, the design of a new dynamic and adaptive ant colony optimization algorithm, the dynamic adaptive strategies in two aspects. First of all, the pheromone evaporation factor set for dynamic adaptive, dynamic changes in the adaptive factor, enhance the searching capability of the algorithm, to avoid falling into local optimal algorithm. Secondly, the multi-constraint conditions to establish the fitness function of the weighted value of fitness function, the influence on the path pheromone update and adaptive factor, enhances the convergence speed of the algorithm. With reference to many simulation experiments, the algorithm has good effect to meet the requirement of multi constrained QoS routing.Secondly, according to the traditional ant colony algorithm is easily trapped into local optimization and the slow speed of convergence in solving the optimal solution, this paper proposes an improved adaptive quantum ant colony algorithm(AQACA). The algorithm design of a new adaptive pheromone update strategy for dynamic, pheromone updating dynamically, using the computational advantages of quantum evolutionary algorithm, improve the speed of convergence, and jump out of local optimal solution.