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    多组播路由问题的粒子群优化算法

    Particle Swarm Optimization for Multiple Multicast Routing Problem

    • 摘要: 具有带宽和时延约束的多组播路由优化问题比组播路由问题更加复杂.为了快速求得多组播路由问题的最优解,提出一种基于树结构演化的粒子群优化算法.粒子由以组播树为分量的向量构成,表示问题的一个可行解,粒子飞行通过树的演化实现.通过在粒子群的环状社会结构中引入粒子视觉半径提高粒子的邻域学习能力;采用树结构变异方法对粒子进行变异提高算法跳出局部解的可能性;根据不满足约束条件的状况对非可行解采取分别惩罚粒子和粒子分量的策略.在随机产生的具有26,50和100个节点的网络拓扑上进行了仿真实验,实验结果表明,提出的算法具有更好的求解质量和较快的收敛速度.

       

      Abstract: The optimization problem of multiple multicast routing with both bandwidth and delay constraints is more complicated than the multicast routing problem. To get the optimal solution of the multiple multicast routing problem quickly, this paper proposes a particle swarm optimization algorithm based on evolution of tree structure. In the proposed algorithm a particle, as a feasible solution of the problem, is represented as a vector, and the components of the particle are represented by tree structure coding. The flight of particles in the search space is implemented through the evolution of trees. Visual radius of a particle is introduced in the orbicular social structure of particle population to enhance the ability of particle neighborhood learning. The tree structure mutation method is designed to increase the possibility of which the algorithm jumps out of local optima. To the infeasible solutions unsatisfied with constraints in the population, penalty strategy is adopted to penalize the particle and its components according to the situation unsatisfied with constraints. Simulation experiments have been carried out on different network topologies produced by random for networks consisting of 26, 50 and 100 nodes. The results of solving the routings of multiple multicast requests show that the proposed algorithm performs better in searching optimal solution and converging speed.

       

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