A Particle Swarm Optimization Approach Based on Monte Carlo Simulation for Solving the Complex Network Reliability Problem

• Published in: IEEE transactions on reliability Vol. 59; no. 1; pp. 212 - 221
• Main Authors: Yeh, Wei-Chang, Lin, Yi-Cheng, Chung, Yuk Ying, Chih, Mingchang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Complex networks; Computer simulation; Cost engineering; Costs; Industrial engineering; Laboratories; Mathematical analysis; Mathematical models; Monte Carlo methods; Monte Carlo simulation; network reliability; network reliability optimization; Networks; Optimization; Particle swarm optimization; Processor scheduling; Reliability; Research and development management; Response surface methodology; series-parallel networks
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2009.2035796

Reliability optimization has been a popular area of research, and received significant attention due to the critical importance of reliability in various kinds of systems. Most network reliability optimization problems are only focused on solving simple structured networks (e.g., series-parallel networks) of which the reliability function can be easily obtained in advance. However, modern networks are usually very complex, and it is impossible to calculate the exact network reliability function by using traditional analytical methods in limited time. Hence, a new particle swarm optimization (PSO) based on Monte Carlo simulation (MCS), named MCS-PSO, has been proposed to solve complex network reliability optimization problems. The proposed MCS-PSO can minimize cost under reliability constraints. To the best of our knowledge, this is the first attempt to use PSO combined with MCS to solve complex network reliability problems without requiring knowledge of the reliability function in advance. Compared with previous works to solve this problem, the proposed MCS-PSO can have better efficiency by providing a better solution to the complex network reliability optimization problem.