Reliability analysis and optimization of weighted voting systems with continuous states input

Weighted voting systems are widely used in many practical fields such as target detection, human organization, pattern recognition, etc. In this paper, a new model for weighted voting systems with continuous state inputs is formulated. We derive the analytical expression for the reliability of the entire system under certain distribution assumptions. A more general Monte Carlo algorithm is also given to numerically analyze the model and evaluate the reliability. This paper further proposes a reliability optimization problem of weighted voting systems under cost constraints. A genetic algorithm is introduced and applied as the optimization technique for the model formulated. A numerical example is then presented to illustrate the ideas.

[1]  Laura Painton,et al.  Genetic algorithms in optimization of system reliability. , 1995 .

[2]  Stuart Bennett,et al.  A taxonomy for software voting algorithms used in safety-critical systems , 2004, IEEE Transactions on Reliability.

[3]  Gregory Levitin,et al.  Multi-State System Reliability - Assessment, Optimization and Applications , 2003, Series on Quality, Reliability and Engineering Statistics.

[4]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[5]  Sherif M. Yacoub Analyzing the behavior and reliability of voting systems comprising tri-state units using enumerated simulation , 2003, Reliab. Eng. Syst. Saf..

[6]  Minge Xie,et al.  Modeling the reliability of threshold weighted voting systems , 2005, Reliab. Eng. Syst. Saf..

[7]  Gregory Levitin Analysis and optimization of weighted voting systems consisting of voting units with limited availability , 2001, Reliab. Eng. Syst. Saf..

[8]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[9]  Gregory Levitin,et al.  The Universal Generating Function in Reliability Analysis and Optimization , 2005 .

[10]  D. Thierens Adaptive mutation rate control schemes in genetic algorithms , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[11]  Gregory Levitin,et al.  Maximizing survivability of vulnerable weighted voting system , 2004, Reliab. Eng. Syst. Saf..

[12]  Yuan-Shun Dai,et al.  Optimal testing-resource allocation with genetic algorithm for modular software systems , 2003, J. Syst. Softw..

[13]  Behrooz Parhami,et al.  THRESHOLD VOTING IS FUNDAMENTALLY SIMPLER THAN PLURALITY VOTING , 1994 .

[14]  Gregory Levitin,et al.  Reliability optimization for weighted voting system , 2001, Reliab. Eng. Syst. Saf..

[15]  Gregory Levitin,et al.  Reliability of fault-tolerant systems with parallel task processing , 2007, Eur. J. Oper. Res..

[16]  Gregory Levitin Weighted voting systems: reliability versus rapidity , 2005, Reliab. Eng. Syst. Saf..

[17]  Hoang Pham,et al.  Weighted voting systems , 1999 .

[18]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[19]  Alice E. Smith,et al.  Penalty guided genetic search for reliability design optimization , 1996 .

[20]  Gregory Levitin,et al.  Asymmetric weighted voting systems , 2002, Reliab. Eng. Syst. Saf..

[21]  Gregory Levitin,et al.  Evaluating correct classification probability for weighted voting classifiers with plurality voting , 2002, Eur. J. Oper. Res..

[22]  Gregory Levitin,et al.  Threshold optimization for weighted voting classifiers , 2003 .