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Multicast routing optimization method based on whale algorithm and its application on spark platform
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An optimization method and whale technology, applied in the field of computer networks, can solve problems such as slow convergence speed and complex multicast routing optimization process.
Active Publication Date: 2020-10-13
SOUTHWEST JIAOTONG UNIV
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[0005] The main purpose of the present invention is to provide a multicast routing optimization method based on the Whale Algorithm, to solve the problems of complex multicast routing optimization process and slow convergence speed in the prior art
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Embodiment 1
[0173] Such as image 3 As shown, the shown network topology has a total of 20 nodes, the node numbers are 0-19, a total of 49 edges, the edge numbers are 0-48 (not shown), and each edge has a corresponding cost value (not shown ), randomly generated within (0,50).
[0174] It is assumed that the source node S of the multicast is 4, and the destination node set D={7, 13, 17}.
[0175] Suppose the whale population scale M=100, according to the step (2) in the multicast routing optimization method based on the above-mentioned whale algorithm to initialize all whale individuals, the position information of each whale is a binary array whose length is 49 (by edge coding), Each bit is 0 or 1, such as [0,1,1,0,1,...,1,0], and the initial fitness value of each whale is 999999.0. Create a globally optimal whale individual gbest outside the population, and its initialization method is described in step (2). Initialize the maximum number of iterations MAX_T=200, and the current numbe...
Embodiment 2
[0242] Embodiment 2, Embodiment 4, and Embodiment 6 all adopt the application of the multicast routing optimization method based on the whale algorithm in the stand-alone environment in the present invention, which is hereinafter referred to as MWOA.
Embodiment 3
[0243] Embodiment 3, Embodiment 5, and Embodiment 7 all adopt the application of the multicast routing optimization method based on the whale algorithm in the present invention on the Spark platform, and the method is hereinafter referred to as PMWOA for short.
[0244] Scenario 1: The number of network topology nodes is 100, the number of edges is 280, the multicast source node is 5, and the multicast destination node set {14, 23, 36, 47, 55, 67, 81, 92, 79}.
[0245] Scenario 2: The number of network topology nodes is 150, the number of edges is 370, the multicast source node is 27, and the multicast destination node set is {33,9,18,67,112,99,137,65,127,141,49,77}.
[0246] Scenario 3: The number of network topology nodes is 200, the number of edges is 542, the multicast source node is 121, and the multicast destination node set {191,7,29,43,167,143,128,97,73,62,14,34,157,108,59,88,111,136}.
[0247] Set the population size as 500 and the number of iterations as 200. Among ...
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Abstract
The invention discloses a multicast routing optimization method based on the whale algorithm. The invention aims at a specific multicast routing optimization problem, and makes further improvements on the whale algorithm to adapt to the solution of the multicast routing optimization problem. The invention uses location information The binary encoding of the optimized continuous problem maps the whale algorithm to a discrete search space, and introduces the ideas of individual crossover, mutation, flavor concentration and other algorithms in the location update strategy, making it more suitable for solving multicast routing problems. This simplifies the multicast routing optimization process and speeds up the convergence. The present invention also discloses the application of a multicast routing optimization method based on the whale algorithm on the Spark platform, thereby enabling the above-mentioned multicast routing optimization method based on the whale algorithm to be parallelized on the Spark platform, greatly speeding up the algorithm. execution speed.
Description
technical field [0001] The invention relates to the technical field of computer networks, in particular to a multicast routing optimization method based on the whale algorithm and its application on the Spark platform. Background technique [0002] In today's rapidly developing Internet field, various novel Internet applications emerge in an endless stream, such as online media, remote conferences, and online games. Most of these interconnected applications require high network resources and short response time, and the data sent by the sender has a lot of repetition. The traditional "one-to-one" transmission cannot meet the real-time and high bandwidth requirements of network applications. The emergence of multicast technology has solved this problem very well. The main feature of multicast is that the sender of the service and multiple receivers build a multicast tree. The sender only needs to send a data packet, and each branch node of the multicast tree copies the data ...
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