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Life testing plans are more vital for carrying out researches on reliability and survival analysis. The inadequacy in the number of testing units or the timing limitations prevents the experiment from being continued until all the failures are detected. Hence, censoring grows to be an inheritably important and well-organized methodology for estimating the model parameters of underlying distributions. Type I and II censoring schemes are the most widely employed censoring schemes. The chief problem associated with the designing of life testing experiments practically is the determination of optimum censoring scheme. Hence, this study attempts to determine the optimum censoring through the minimization of total cost spent for the experiment, consuming less termination time and reasonable number of failures. The ABC algorithm is being employed in this study for obtaining the optimal censoring schemes. Entropy and variance serves as the optimal criterion. The proposed method utilizes Risk analysis to evaluate the efficiency or reliability of the optimal censoring scheme that is being determined. Optimum censoring scheme indicates the process of determining the best scheme from among the entire censoring schemes possible, in accordance to a specific optimality criterion.

Optimal Censoring Scheme Selection Based on Artificial Bee Colony Optimization (ABC) Algorithm