Reliability-Redundancy Allocation Using Random Walk Gray Wolf Optimizer

From some past recent years, Swarm Intelligence (SI) based optimization algorithms have shown their impact in finding the efficient solutions of real-life application problems that occur in engineering, science, industry, and in various other fields. Gray Wolf Optimizer (GWO) is an efficient and popular optimizer in the area of SI to solve nonlinear complex optimization problems. GWO mimics the dominant leadership characteristic of gray wolves to catch the prey. But, like other stochastic search algorithms, GWO gets trapped in local optimums in some cases. Therefore in the present study, Random Walk Gray Wolf Optimizer (RW-GWO) is applied to determine-(1) the optimal redundancies to optimize the system reliability with constraints on volume, weight, and system cost in series, series-parallel, and complex bridge systems and (2) the optimum cost of two different types of complex systems with constraints imposed on system reliability. The obtained results are compared with classical GWO and some other optimization algorithms that are used to solve reliability problems in the literature. The comparison shows that the RW-GWO is comparatively an efficient algorithm to solve the reliability engineering problems.