Wu, L.-Y., Chen, W.-N., Deng, H.-H., Zhang, J. and Li, Y. (2016) Particle swarm optimization with Monte-Carlo simulation and hypothesis testing for network reliability problem. In: 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI), Chiang Mai, Thailand, 14-16 Feb 2016, pp. 310-317. ISBN 9781467377805 (doi: 10.1109/ICACI.2016.7449844)
|
Text
118972.pdf - Accepted Version 360kB |
Abstract
The performance of Monte-Carlo Simulation(MCS) is highly related to the number of simulation. This paper introduces a hypothesis testing technique and incorporated into a Particle Swarm Optimization(PSO) based Monte-Carlo Simulation(MCS) algorithm to solve the complex network reliability problem. The function of hypothesis testing technique is to reduce the dispensable simulation in network system reliability estimation. The proposed technique contains three components: hypothesis testing, network reliability calculation and PSO algorithm for finding solutions. The function of hypothesis testing is to abandon unpromising solutions; we use Monte-Carlo simulation to obtain network reliability; since the network reliability problem is NP-hard, PSO algorithm is applied. Since the execution time can be better decreased with the decrease of Confidence level of hypothesis testing in a range, but the solution becomes worse when the confidence level exceed a critical value, the experiment are carried out on different confidence levels for finding the critical value. The experimental results show that the proposed method can reduce the computational cost without any loss of its performance under a certain confidence level.
| Item Type: | Conference Proceedings |
|---|---|
| Additional Information: | This work was supported in part by the NSFC projects Nos. 61379061, 61332002, 6141101191, in part by Natural Science Foundation of Guangdong for Distinguished Young Scholars No. 2015A030306024, in part by the Guangdong Special Support Program No. 2014TQ01X550, and in part by the Guangzhou Pearl River New Star of Science and Technology No. 151700098. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Li, Professor Yun |
| Authors: | Wu, L.-Y., Chen, W.-N., Deng, H.-H., Zhang, J., and Li, Y. |
| College/School: | College of Science and Engineering > School of Engineering > Systems Power and Energy |
| ISBN: | 9781467377805 |
| Copyright Holders: | Copyright © 2016 IEEE |
| Related URLs: |
University Staff: Request a correction | Enlighten Editors: Update this record
Deposit and Record Details
Deposit and Record Details