Abstract
In general, the problems dealt in reliability engineering need optimization and are NP-hard in nature. For a long time, the reliability optimization problems (addressed through optimal redundancy allocation, reliability allocation, or reliability-redundancy allocation) have drawn the interest of the reliability research community to develop better and efficient solutions. In recent years, substantial efforts have been made by the researchers in applying nature-inspired algorithms for finding an optimal or near-optimal solution for such problems. This chapter briefly reviews some recently developed nature-inspired meta-heuristic algorithms having potential applications in the field of reliability engineering. It provides the details of a widely used meta-heuristic, namely, Gray Wolf Optimizer (GWO), and showcases its applications in solving the reliability optimization problems. More specifically, this chapter presents the solution of 10 different system reliability optimization problems using GWO and compares the results obtained through other well-known techniques. The resulting simulation results show that the solutions generated by the GWO are either optimal or comparable to the best solutions attained by other techniques. This shows the feasibility of applying other variants of GWO algorithm in complex engineering problems of the contemporary world.
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