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Using Reference Point-Based NSGA-II to System Reliability
Open Access   Article

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: hemantkumar2654@gmail.com.

Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-12 , Page no. 7-14, Dec-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i12.714

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.
 
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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.
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Abstract :
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)
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