A CA-SVM Based Monte Carlo Approach for Evaluating Complex Network Reliability

Yuan-peng Ruan () and Zhen He
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Yuan-peng Ruan: Tianjin University
Zhen He: Tianjin University

Chapter Chapter 66 in The 19th International Conference on Industrial Engineering and Engineering Management, 2013, pp 609-617 from Springer

Abstract: Abstract Many real-world complex systems can be modeled as networks. Evaluation of network reliability plays an important role in engineering applications. When evaluating the S-T complex network reliability, the traditional approaches may bring about the problems of increasing computational complexity or decreasing the calculation accuracy. This paper proposes a CA-SVM based Monte Carlo approach based on the drawbacks of traditional approaches. Support Vector Machine (SVM) is a fast and efficient algorithm to ascertain the network connectivity in simulation process. Cellular automata (CA) is used for creating training data points, which speeds up the computing process. Particle swam optimization (PSO) is used for parameters selection of SVM, which increases the accuracy of the result. An example is shown to illustrate the proposed approach.

Keywords: Complex network reliability; Support vector machine; Cellular automata; Particle swarm optimization (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-38433-2_66

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DOI: 10.1007/978-3-642-38433-2_66

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