Random walk search procedures for reliability optimization of systems with fault tolerance

In this paper we develop two efficient discrete stochastic search methods based on random walk procedure for maximizing system reliability subjected to imperfect fault coverage where uncovered component failures cause immediate system failure, even in the presence of adequate redundancy. The first search method uses a sequential sampling procedure with fixed boundaries at each iteration. We show that this search process satisfies local balance equations and its equilibrium distribution gives most weight to the optimal solution. We also show that the solution that has been visited most often in the first m iterations converges almost surely to the optimal solution. The second search method uses a sequential sampling procedure with increasing boundaries at each iteration. We show that if the increase occurs slower than a certain rate, this search process will converge to the optimal set with probability 1. We consider the system where reliability cannot be evaluated exactly but must be estimated through Monte Carlo simulation. Copyright © 2008 John Wiley & Sons, Ltd.