Int J Performability Eng ›› 2009, Vol. 5 ›› Issue (3): 227-234.doi: 10.23940/ijpe.09.3.p227.mag
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AMAR KISHOR1, SHIV PRASAD YADAV1, and SURENDRA KUMAR2
Abstract:
This paper considers the allocation of maximum reliability to a complex system, while minimizing the cost of the system, a type of multi-objective optimization problem (MOOP). Multi-objective Evolutionary Algorithms (MOEAs) have been shown in the last few years as powerful techniques to solve MOOP .This paper successfully applies a Nondominated sorting genetic algorithm (NSGA-II) technique to obtain the Pareto optimal solution of a complex system reliability optimization problem under fuzzy environment in which the statements might be vague or imprecise. Decision-maker (DM) could choose, in a "posteriori" decision environment, the most convenient optimal solution according to his/her level of satisfaction. The efficiency of NSGA-II in solving this problem is demonstrated by comparing its results with those of simulated annealing (SA) and nonequilibrium simulated annealing (NESA).Received on November 1, 2007, revised on October 24, 2008References: 14
AMAR KISHOR, SHIV PRASAD YADAV, and SURENDRA KUMAR. A Multi-objective Genetic Algorithm for Reliability Optimization Problem [J]. Int J Performability Eng, 2009, 5(3): 227-234.
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URL: https://www.ijpe-online.com/EN/10.23940/ijpe.09.3.p227.mag
https://www.ijpe-online.com/EN/Y2009/V5/I3/227