A Multi-objective Genetic Algorithm for Reliability Optimization Problem
Volume 5, Number 3, April 2009 - Paper 3 - pp. 227 - 234
AMAR KISHOR1, SHIV PRASAD YADAV1 and SURENDRA KUMAR21Department of Mathematics, I.I.T Roorkee, Roorkee, India:247667
2Department of Electrical Engineering, I.I.T Roorkee, Roorkee, India:247667
(Received on November 1, 2007, revised on October 24, 2008)
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).
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