Series-Parallel Systems Reliability Optimization Using Genetic Algorithm and Statistical Analysis
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Series-Parallel Systems Reliability Optimization Using Genetic Algorithm and Statistical Analysis

Authors: Essa Abrahim Abdulgader Saleem, Thien-My Dao


The main objective of this paper is to optimize series-parallel system reliability using Genetic Algorithm (GA) and statistical analysis; considering system reliability constraints which involve the redundant numbers of selected components, total cost, and total weight. To perform this work, firstly the mathematical model which maximizes system reliability subject to maximum system cost and maximum system weight constraints is presented; secondly, a statistical analysis is used to optimize GA parameters, and thirdly GA is used to optimize series-parallel systems reliability. The objective is to determine the strategy choosing the redundancy level for each subsystem to maximize the overall system reliability subject to total cost and total weight constraints. Finally, the series-parallel system case study reliability optimization results are showed, and comparisons with the other previous results are presented to demonstrate the performance of our GA.

Keywords: Genetic algorithm, optimization, reliability, statistical analysis.

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[1] Boyabatli, O., & Sabuncuoglu, I. (2004). Parameter selection in genetic algorithms. Journal of Systemics, Cybernetics and Informatics, 4(2), 78.
[2] Castillo-Valdivieso, P. A., Merelo, J. J., Prieto, A., Rojas, I., & Romero, G. (2002). Statistical analysis of the parameters of a neuro-genetic algorithm. IEEE Transactions on Neural Networks, 13(6), 1374-1394.
[3] Chambari, A., Rahmati, S. H. A., & Najafi, A. A. (2012). A bi-objective model to optimize reliability and cost of system with a choice of redundancy strategies. Computers & Industrial Engineering, 63(1), 109-119.
[4] Chern, M. S. (1992). On the computational complexity of reliability redundancy allocation in a series system. Operations research letters, 11(5), 309-315.
[5] Coit, D. W., & Smith, A. E. (1996a). Reliability optimization of series-parallel systems using a genetic algorithm. Reliability, IEEE Transactions on, 45(2), 254-260.
[6] Coit, D. W., & Smith, A. E. (1996b). Penalty guided genetic search for reliability design optimization. Computers & industrial engineering, 30(4), 895-904.
[7] François, O., & Lavergne, C. (2001). Design of evolutionary algorithms-A statistical perspective. IEEE Transactions on Evolutionary Computation, 5(2), 129-148.
[8] Khorshidi, H. A., & Nikfalazar, S. (2015). Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization. Applied and Computational Mathematics, 4(2-1), 1-6.
[9] Kuo, W., & Prasad, V. R. (2000). An annotated overview of system-reliability optimization. Reliability, IEEE Transactions on, 49(2),176-187.
[10] Kuri-Morales, A. F., & Gutiérrez-García, J. (2002, April). Penalty function methods for constrained optimization with genetic algorithms: A statistical analysis. In Mexican International Conference on Artificial Intelligence (pp. 108-117). Springer Berlin Heidelberg.
[11] Liang, Y. C., & Chen, Y. C. (2007). Redundancy allocation of series- parallel systems using a variable neighborhood search algorithm. Reliability Engineering & System Safety, 92(3), 323-331.
[12] Mills, K. L., Filliben, J. J., & Haines, A. L. (2015). Determining relative importance and effective settings for genetic algorithm control parameters. Evolutionary computation, 23(2), 309-342.
[13] Petrovski, A., Brownlee, A. E. I., & McCall, J. (2005). Statistical optimisation and tuning of GA factors. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2005), Volume 1. IEEE.
[14] Sharifi, M., & Yaghoubizadeh, M. (2015). Reliability Modelling of the Redundancy Allocation Problem in the Series-parallel Systems and Determining the System Optimal Parameters. Journal of Optimization in Industrial Engineering, 8(17), 67-77.
[15] Smith, A. E., & Coit, D. W. (1997). Penalty functions. Handbook on Evolutionary Computation, pages C, 5, 1-6.
[16] Tillman, F. A., Hwang, C. L., & Kuo, W. (1977). Optimization Techniques for System Reliability with Redundancy A Review. Reliability, IEEE Transactions on, 26(3), 148-155.
[17] Yeniay, Ö. (2005). Penalty function methods for constrained optimization with genetic algorithms. Mathematical and Computational Applications, 10(1), 45-56.
[18] Zhao, J. H., Liu, Z., & Dao, M. T. (2007). Reliability optimization using multi-objective ant colony system approaches. Reliability Engineering & System Safety, 92(1), 109-120.