This article addresses the system reliability optimization problem as reliability–redundancy allocation problem, aiming to maximize the system reliability through a trade-off between redundancy levels and the reliability of the components. In this study, cold-standby strategy has been considered for the redundant components, and a population-based meta-heuristic algorithm, called stochastic fractal search, is applied to solve different benchmark problems. Using the proposed stochastic fractal search algorithm, all the benchmark problems are improved and new structures with higher reliability values have been found. The experimental results reveal the superiority of the proposed stochastic fractal search algorithm in terms of quality and robustness of the solutions in cold-standby redundancy case compared to all previous studies.

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