A CELLULAR AUTOMATA HYBRID QUASI-RANDOM MONTE CARLO SIMULATION FOR ESTIMATING THE ONE-TO-ALL RELIABILITY OF ACYCLIC MULTI-STATE INFORMATION NETWORKS

作者：Yeh Wei Chang^*; Cao Longbing; Jin Jesse S

来源：International Journal of Innovative Computing Information and Control, 2012, 8(3B): 2001-2014.

• Network reliability
• Multi-state node acyclic network (AMIN)
• Hybrid quasi monte carlo method
• Minimal tree/cut

Many real-world systems (such as cellular telephones and transportation) are acyclic multi-state information networks (AMIN). These networks are composed of multi-state nodes, with different states determined by a set of nodes that receive a signal directly from these multi-state nodes, without satisfying the conservation law. Evaluating the AMIN reliability arises at the design and exploitation stage of many types of technical systems. However, existing analytical methods fail to estimate AMIN reliability in a realistic time frame, even for smaller-sized AMINs. Hence, the main purpose of this article is to present a cellular automata hybrid quasi-Monte Carlo simulation (CA-HMC) by combining cellular automata (CA, to rapidly determine network states), pseudo-random sequences (PRS, to obtain the flexibility of the network) and quasi-random sequences (QRS, to improve the accuracy) to obtain a high-quality estimation of AMIN reliability M order to improve the calculation efficiency. We use one benchmark example from well-known algorithms in literature to show the utility and performance of the proposed CA-HMC simulation when evaluating the one-to-all AMIN reliability.

• 出版日期2012-3
• 单位清华大学