基于自反学习粒子群优化的动态QoS路由算法

A dynamic QoS routing algorithm based on self-opposition-based learning particle swarm optimization

在动态Internet环境下,尽力而为的服务模式在传输数据时会造成路径过期问题。提出一种自反学习粒子群优化算法。该算法通过自反学习策略,扩大每个粒子搜索范围,从而使得算法能快速收敛。该算法的主要优势在于能够克服传统粒子群在动态环境下无法收敛问题。实验表明,该算法相对当前一些经典算法具有较好的性能。

Best-effort service mode clauses the problem of overdue path in the data transmission on the dynamic Internet. This paper introduces self-opposition-based learning particle swarm optimization algorithm. The algorithm expends eac;h particle searching area acxording to self-opposition-based learning and advantage of the algorithm improves swarm searching efficiency. The experiment shows that the algorithm has better performance comparing with other classical algorithms.