A tree-based particle swarm optimization for multicast routing |
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Authors: | Hua Wang Xiangxu Meng Shuai Li Hong Xu |
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Affiliation: | 1. Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Taiwan;2. Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan;3. Center for Distributed and Mobile Computing, EECS, University of Cincinnati, OH 45221-0030, United States;1. Electronics and Telecommunications Research Institute (ETRI), Daejeon, Republic of Korea;2. The Affiliated Institute of ETRI, Daejeon, Republic of Korea;3. Department of Computer Science in Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea;1. Instituto de Investigación en Comunicación Óptica-Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Álvaro Obregón 64, 78000 San Luis Potosí, S.L.P., Mexico;2. Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Álvaro Obregón 64, 78000 San Luis Potosí, S.L.P., Mexico;3. Coordinación Académica Región Altiplano Oeste, Universidad Autónoma de San Luis Potosí, Álvaro Obregón 64, 78000 San Luis Potosí, S.L.P., Mexico;4. Instituto Potosino de Investigacion Cientifica y Tecnologica, Camino a la presa San José 2055, Col. Lomas 4a Sección, 78216, San Luis Potosí, S.L.P., Mexico |
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Abstract: | QoS multicast routing is a non-linear combinatorial optimization problem. It tries to find a multicast routing tree with minimal cost that can satisfy constraints such as bandwidth, delay, and delay jitter. This problem is NP-complete. The solution to such problems is often to search first for paths from the source node to each destination node and then integrate these paths into a multicast tree. Such a method, however, is slow and complex. To overcome these shortcomings, we propose a new method for tree-based optimization. Our algorithm optimizes the multicast tree directly, unlike the conventional solutions to finding paths and integrating them to generate a multicast tree. Our algorithm also applies particle swarm optimization to the solution to control the optimization orientation of the tree shape. Simulation results show that our algorithm performs well in searching, converging speed and adaptability scale. |
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