摘要
分析了具有网络时延和时延抖动限制的动态组播路由问题的数学模型。在此模型的基础上提出了一种基因库(GP)与传统遗传算法(GA)混合的优化算法GP-GA。该算法利用基因库保存进化过程中得到的解路径以指导后继进化过程,同时改进了交叉和变异算子来加快算法的收敛速度。考虑到问题可能陷入的局部最优情况,又构造了基于“保留和不保留”的进化控制策略来增强寻优能力,很大程度上避免了算法“早熟”现象的发生。大量的仿真实验表明:GP-GA算法相对现有的遗传算法求得最优解的概率更高,相对于动态的组播环境也有很好的代价性能。
The mathematic model of dynamic multicast muting with Nodes delay and delay variation constraints was analyzed. Based on the model, an optimization algorithm called GP-GA was proposed by hybridizing Gcne-Pool （GP） with traditional Genetic Algorithm （GA）. This method made use of the gene-pool to save the solutions during the process so as to direct the remaining evolution. In the mean time, the crossover and mutation operator were improved to accelerate the convergence speed. Considering that the problem may be trapped by local optimization easily, the evolution strategy based-on ＂reserved and non-reserved＂ was also constructed to enhance the ability of finding optimal solution and decrease the probability of ＂premature＂ phenomena commendably. A great number of simulations demonstrate that the probability of GP-GA converging optimal solutions is higher than general GA, and the algorithm is also effective for being adjusted to the dynamic mulficast routing.
出处
《计算机应用》
CSCD
北大核心
2006年第8期1947-1949,1952,共4页
journal of Computer Applications
基金
中国地质大学(武汉)优秀青年教师资助计划资助项目(CUGQNL44)
关键词
STEINER树
动态组播路由
基因库
遗传算法
路由优化
Steiner tree
dynamic multicast routing
Gene-Pool（GP）
Genetic Algorithm（GA）
routing optimization
