|Using Reference Point-Based NSGA-II to System Reliability|
|H. Kumar1 , S.P. Yadav2|
1 Department of Mathematics, I.I.T. Roorkee, Roorkee, India.
2 Department of Mathematics, I.I.T. Roorkee, Roorkee, India.
|Correspondence should be addressed to: firstname.lastname@example.org.|
Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-12 , Page no. 7-14, Dec-2017
Online published on Dec 31, 2017
Copyright © H. Kumar, S.P. Yadav . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
|View this paper at Google Scholar | DPI Digital Library|
|XML View||PDF Download|
IEEE Style Citation: H. Kumar, S.P. Yadav, “Using Reference Point-Based NSGA-II to System Reliability”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.7-14, 2017.
MLA Style Citation: H. Kumar, S.P. Yadav "Using Reference Point-Based NSGA-II to System Reliability." International Journal of Computer Sciences and Engineering 5.12 (2017): 7-14.
APA Style Citation: H. Kumar, S.P. Yadav, (2017). Using Reference Point-Based NSGA-II to System Reliability. International Journal of Computer Sciences and Engineering, 5(12), 7-14.
|39||50 downloads||7 downloads|
|In principle, a multi-objective optimization problem (MOOP) provides a group of non-dominated solutions (popularly known as Pareto-optimal solutions) for the decision maker (DM). A DM is undecidable to claim one of these solutions to be better than another in the absence of any further information. Due to this reason, a DM needs as many Pareto-optimal solutions as possible. Classical optimization methods are unable to produce multiple solutions at a time because of converting the MOOP to a single-objective optimization problem (SOOP). In the past decades, multi-objective evolutionary algorithms (MOEAs) have been developed to be powerful techniques of identifying a complete picture of the Pareto-optimal solutions space, where a DM can select one out of these solutions according to his/her preference. Moreover, a more efficient MOEA can exploit the search in a better position if the DM provides some general views or ideas about the solution in terms of reference points or weights. Reference point based NSGA-II (R-NSGA-II) is such type of an MOEA where DM’s assigned reference points are used to search the solutions and its diversity is controlled efficiently. This paper presents the applicability of the R-NSGA-II algorithm to the system reliability design problem. The simulation results show the advantage of R-NSGA-II over NSGA-II.|
|Key-Words / Index Term :|
|Multi-objective optimization problem (MOOP), Multi-objective evolutionary algorithms (MOEAs), Reference points, System reliability, Pareto-optimal front (POF)|
 K. Deb, “Multi-objective optimization using evolutionary algorithms”, John Wiley & Sons, 2001.
 J. Knowles, D. Corne, “The Pareto archived evolution strategy: A new baseline algorithm for multiobjective optimization”, In Proceedings of the 1999 Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, DOI: 10.1109/CEC.1999.781913, 1999.
 N. Srinivas, K. Deb, “Multi-objective optimization using non-dominated sorting in genetic algorithms”, Evol. Comput., Vol. 2, no. 3, pp. 221-248, 1994.
 J. Horn, N. Nafploitis, D. Goldberg, “A niched Pareto genetic algorithm for multi-objective optimization”, In Proceedings of the First IEEE Conference on Evolutionary Computation, pp. 82-87, 1994.
 E. Zitzler, L. Thiele, “An evolutionary algorithm for multi-objective optimization: The strength Pareto approach”, Technical report 43, Zurich, Switzerland: Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), 1998.
 K. Deb, S. Agarwal, A. Pratap, T. Meyarivan, “A fast and elitist multi-objective genetic algorithm: NSGA-II”, IEEE Trans. Evol. Comput., Vol. 6, pp. 182-197, 2002.
 K. Deb, J. Sundar, U.B. Rao, S. Chaudhuri, “Reference Point Based Multi-Objective Optimization Using Evolutionary Algorithms”, International Journal of Computational Intelligence Research, Vol. 2, No. 3, pp. 273-286, 2006.
 H. Garg, S.P. Sharma, “Multi-objective reliability-redundancy allocation problem using particle swarm optimization”, Computers & Industrial Engineering, Vol. 64, No. 1, pp. 247-255, 2012.
 G.D. Goldberg, “Genetic algorithms for search, optimization, and machine learning”, Reading, MA: Addison-Wesley, 1989.
 D. Salazar, C.M. Rocco, B. J. Galvan, “Optimization of constrained multiple objective reliability problems using evolutionary algorithms”, Reliability Engineering and System Safety, 91, pp. 1057-1070, 2006.
 A. Kishore, S. P. Yadav, S. Kumar, “Application of a Multi-objective Genetic Algorithm to solve Reliability Optimization Problem”, International Conference on Computational Intelligence and Multimedia Applications, pp. 458-462, DOI: 10.1109/ICCIMA, 2007.
 A. Kishore, S. P. Yadav, S. Kumar, “Interactive fuzzy multiobjective optimization using NSGA-II”, OPSEARCH, Vol. 46, No. 2, pp. 214-224, 2009.
 Z. Wang, T. Chen, K. Tang., X. Yao, “A Multi-objective Approach to Redundancy Allocation Problem in Parallel-series Systems”, IEEE, pp. 582-589, DOI: 978-1-4244-2959-2/09, 2009.
 J. Safari, “Multi-objective reliability optimization of series-parallel systems with a choice of redundancy strategies”, Reliab Eng Syst Saf., 108, pp. 10–20, 2012.
 K. Khalili-Damghani, A. R. Abtahi, M. Tavana, “A decision support system for solving multi-objective redundancy allocation problems”, Qual Reliab Eng Int, Vol. 30, No. 8, pp. 1249-1262, 2014.
 A. Taboada, F. Baheranwala, D.W. Coit, “Practical solutions for multi-objective optimization: An application to system reliability design problems”, Rel. Engg. Syst. Saft., 92, pp. 314-322, 2007.
 K.K. Aggarwal, J.S. Gupta, “On minimizing the cost of reliable systems”, IEEE Transaction on Reliability R-24 (3), pp. 205, 1975.
 V. Ravi, B.S.N. Murthy, P.J. Reddy, “Nonequilibrium simulated annealing algorithm applied to reliability optimization of complex systems”, IEEE Trans. On Rel., Vol. 46, No. 2, pp. 233-239, 2000.