Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization

Hadi Akbarzade Khorshidi; Sanaz Nikfalazar

doi:doi:10.11648/j.acm.s.2015040201.11

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Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization

Hadi Akbarzade Khorshidi, Sanaz Nikfalazar

Published in Applied and Computational Mathematics (Volume 4, Issue 2-1)

Received: 21 January 2015 Accepted: 22 January 2015 Published: 2 March 2015

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Abstract

Using meta-heuristic approaches to solve reliability and redundancy allocation problems (RRAP) has become attractive for researchers in recent years. In this paper, an optimization model is presented to maximize system reliability and minimize system cost simultaneously for multi-state weighted k-out-of-n systems. The model tends to optimize system design and maintenance activities over functioning periods that provides a dynamic modeling. A recently developed meta-heuristic approach imperialist competitive algorithm (ICA) and genetic algorithm (GA) are used to solve the model. The computational results have been compared to find out which approach is more appropriate for solving complex system reliability optimization models. It is shown that GA can find the better solution while ICA is a faster approach. In addition, an investigation is done on different parameters of the ICA.

Published in	Applied and Computational Mathematics (Volume 4, Issue 2-1) This article belongs to the Special Issue Quality, Reliability, Safety, and Risk Modeling and Optimization
DOI	10.11648/j.acm.s.2015040201.11
Page(s)	1-6
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Reliability-Redundancy Allocation Problem (RRAP), Imperialist Competitive Algorithm (ICA), Genetic Algorithm (GA), System Reliability Optimization (SRO), Multi-State Weighted k-out-of-n Systems

References

[1]	Khorshidi, H. A., Gunawan, I. & Ibrahim, M. Y., Investigation on system reliability optimization based on classification of criteria.in Proc. IEEE International Conference on Industrial Technology (ICIT), 2013.
[2]	Chern, M. S., On the computational complexity of reliability redundancy allocation in a series system, Operations Research Letters 1992; 11 (5), 309-315.
[3]	Ebrahimipur, V., Qurayshi, S. F., Shabani, A. & Maleki-Shoja, B., Reliability optimization of multi-state weighted k-out-of-n systems by fuzzy mathematical programming and genetic algorithm, International Journal of System Assurance Engineering and Management 2011; 2 (4), 312-318.
[4]	Hamadani, A. Z. & Khorshidi, H. A., System reliability optimization using time value of money, International Journal of Advanced Manufacturing Technology 2013; 66 (1-4), 97-106.
[5]	Konak, A., Coit, D. W. & Smith, A. E., Multi-objective optimization using genetic algorithms: A tutorial, Reliability Engineering and System Safety 2006; 91 (9), 992-1007.
[6]	Coelho, L. D. S., An efficient particle swarm approach for mixed-integer programming in reliability-redundancy optimization applications, Reliability Engineering and System Safety 2009; 94 (4), 830-837.
[7]	Di, P., Xu, Y. F., Li, F. & Chen, T. 2014. Reliability optimization for multi-state series-parallel system design using ant colony algorithm. Applied Mechanics and Materials.
[8]	Afonso, L. D., Mariani, V. C. & Dos Santos Coelho, L., Modified imperialist competitive algorithm based on attraction and repulsion concepts for reliability-redundancy optimization, Expert Systems with Applications 2013; 40 (9), 3794-3802.
[9]	Sakalli, U. S., A simulated annealing approach for reliability-based chance-constrained programming, Applied Stochastic Models in Business and Industry 2014; 30 (4), 497-508.
[10]	Najafi, A. A., Karimi, H., Chambari, A. & Azimi, F., Two metaheuristics for solving the reliability redundancy allocation problem to maximize mean time to failure of a series-parallel system, Scientia Iranica 2013; 20 (3), 832-838.
[11]	Sharifi, M., Mousa Khani, M. & Zaretalab, A., Comparing Parallel Simulated Annealing, Parallel Vibrating Damp Optimization and Genetic Algorithm for Joint Redundancy-Availability Problems in a Series-Parallel System with Multi-State Components, Journal of Optimization in Industrial Engineering 2014; 7 (14), 13-26.
[12]	Li, W. & Zuo, M. J., Reliability evaluation of multi-state weighted k-out-of-n systems, Reliability Engineering and System Safety 2008; 93 (1), 161-168.
[13]	Khorshidi, H. A., Gunawan, I. & Ibrahim, M. Y., Multi-objective optimization model on reliability-redundancy allocation problem in multi-state weighted k-out-of-n system, IEEE Transactions on Industrial Informatics 2015; (in press)
[14]	Khorshidi, H. A., Gunawan, I. & Ibrahim, M. Y., On Reliability Evaluation of Multistate Weighted k-Out-of-n System Using Present Value, Engineering Economist 2015; 60 (1), 22-39.
[15]	Atashpaz-Gargari, E. & Lucas, C., Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition, 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007, 4661-4667.
[16]	Azad, H. R. L., Boushehri, N. S. & Mollaverdi, N., Investigating the application of opposition concept to colonial competitive algorithm, International Journal of Bio-Inspired Computation 2012; 4 (5), 319-329.

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APA Style

Hadi Akbarzade Khorshidi, Sanaz Nikfalazar. (2015). Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization. Applied and Computational Mathematics, 4(2-1), 1-6. https://doi.org/10.11648/j.acm.s.2015040201.11

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ACS Style

Hadi Akbarzade Khorshidi; Sanaz Nikfalazar. Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization. Appl. Comput. Math. 2015, 4(2-1), 1-6. doi: 10.11648/j.acm.s.2015040201.11

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AMA Style

Hadi Akbarzade Khorshidi, Sanaz Nikfalazar. Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization. Appl Comput Math. 2015;4(2-1):1-6. doi: 10.11648/j.acm.s.2015040201.11

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@article{10.11648/j.acm.s.2015040201.11,
  author = {Hadi Akbarzade Khorshidi and Sanaz Nikfalazar},
  title = {Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization},
  journal = {Applied and Computational Mathematics},
  volume = {4},
  number = {2-1},
  pages = {1-6},
  doi = {10.11648/j.acm.s.2015040201.11},
  url = {https://doi.org/10.11648/j.acm.s.2015040201.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.s.2015040201.11},
  abstract = {Using meta-heuristic approaches to solve reliability and redundancy allocation problems (RRAP) has become attractive for researchers in recent years. In this paper, an optimization model is presented to maximize system reliability and minimize system cost simultaneously for multi-state weighted k-out-of-n systems. The model tends to optimize system design and maintenance activities over functioning periods that provides a dynamic modeling. A recently developed meta-heuristic approach imperialist competitive algorithm (ICA) and genetic algorithm (GA) are used to solve the model. The computational results have been compared to find out which approach is more appropriate for solving complex system reliability optimization models. It is shown that GA can find the better solution while ICA is a faster approach. In addition, an investigation is done on different parameters of the ICA.},
 year = {2015}
}

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TY  - JOUR
T1  - Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization
AU  - Hadi Akbarzade Khorshidi
AU  - Sanaz Nikfalazar
Y1  - 2015/03/02
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T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
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PB  - Science Publishing Group
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AB  - Using meta-heuristic approaches to solve reliability and redundancy allocation problems (RRAP) has become attractive for researchers in recent years. In this paper, an optimization model is presented to maximize system reliability and minimize system cost simultaneously for multi-state weighted k-out-of-n systems. The model tends to optimize system design and maintenance activities over functioning periods that provides a dynamic modeling. A recently developed meta-heuristic approach imperialist competitive algorithm (ICA) and genetic algorithm (GA) are used to solve the model. The computational results have been compared to find out which approach is more appropriate for solving complex system reliability optimization models. It is shown that GA can find the better solution while ICA is a faster approach. In addition, an investigation is done on different parameters of the ICA.
VL  - 4
IS  - 2-1
ER  -

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Author Information

Hadi Akbarzade Khorshidi

School of Applied Science and Engineering, Faculty of Science, Monash University, Melbourne, Australia
Sanaz Nikfalazar

Faculty of Management, Department of Public Administration, University of Tehran, Tehran, Iran

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APA Style

Hadi Akbarzade Khorshidi, Sanaz Nikfalazar. (2015). Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization. Applied and Computational Mathematics, 4(2-1), 1-6. https://doi.org/10.11648/j.acm.s.2015040201.11

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ACS Style

Hadi Akbarzade Khorshidi; Sanaz Nikfalazar. Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization. Appl. Comput. Math. 2015, 4(2-1), 1-6. doi: 10.11648/j.acm.s.2015040201.11

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AMA Style

Hadi Akbarzade Khorshidi, Sanaz Nikfalazar. Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization. Appl Comput Math. 2015;4(2-1):1-6. doi: 10.11648/j.acm.s.2015040201.11

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@article{10.11648/j.acm.s.2015040201.11,
  author = {Hadi Akbarzade Khorshidi and Sanaz Nikfalazar},
  title = {Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization},
  journal = {Applied and Computational Mathematics},
  volume = {4},
  number = {2-1},
  pages = {1-6},
  doi = {10.11648/j.acm.s.2015040201.11},
  url = {https://doi.org/10.11648/j.acm.s.2015040201.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.s.2015040201.11},
  abstract = {Using meta-heuristic approaches to solve reliability and redundancy allocation problems (RRAP) has become attractive for researchers in recent years. In this paper, an optimization model is presented to maximize system reliability and minimize system cost simultaneously for multi-state weighted k-out-of-n systems. The model tends to optimize system design and maintenance activities over functioning periods that provides a dynamic modeling. A recently developed meta-heuristic approach imperialist competitive algorithm (ICA) and genetic algorithm (GA) are used to solve the model. The computational results have been compared to find out which approach is more appropriate for solving complex system reliability optimization models. It is shown that GA can find the better solution while ICA is a faster approach. In addition, an investigation is done on different parameters of the ICA.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - Comparing Two Meta-Heuristic Approaches for Solving Complex System Reliability Optimization
AU  - Hadi Akbarzade Khorshidi
AU  - Sanaz Nikfalazar
Y1  - 2015/03/02
PY  - 2015
N1  - https://doi.org/10.11648/j.acm.s.2015040201.11
DO  - 10.11648/j.acm.s.2015040201.11
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.s.2015040201.11
AB  - Using meta-heuristic approaches to solve reliability and redundancy allocation problems (RRAP) has become attractive for researchers in recent years. In this paper, an optimization model is presented to maximize system reliability and minimize system cost simultaneously for multi-state weighted k-out-of-n systems. The model tends to optimize system design and maintenance activities over functioning periods that provides a dynamic modeling. A recently developed meta-heuristic approach imperialist competitive algorithm (ICA) and genetic algorithm (GA) are used to solve the model. The computational results have been compared to find out which approach is more appropriate for solving complex system reliability optimization models. It is shown that GA can find the better solution while ICA is a faster approach. In addition, an investigation is done on different parameters of the ICA.
VL  - 4
IS  - 2-1
ER  -

Copy | Download