Computation of optimal Type-II progressive censoring scheme using genetic algorithm approach

The experimenter must perform a legitimate search in the entire set of feasible censoring schemes to identify the optimal Type-II progressive censoring scheme, when applied to a life-testing experiment. Current recommendations are limited to small sample sizes. Exhaustive search strategies are not practically feasible for large sample sizes. This paper proposes a meta-heuristic algorithm based on the genetic algorithm for large sample sizes. The algorithm is found to provides exactly the same solution for small sample sizes as the solution obtained in an exhaustive search; however, for large sample sizes, it provides optimal or near-optimal solutions. Our suggested optimal criterion is based on the cost function and is scale-invariant for both location-scale and log-location-scale distribution families. To investigate how inaccurate parameter values or cost coefficients may affect the optimal solution, a sensitivity analysis is also taken into account.