Analysis and optimization of system reliability have very much importance for developing an optimal design for the system while using the available resources. Several studies are centered towards reliability optimization using metaheuristics. In this study, a recently developed metaheuristic optimization algorithm called hybrid PSO-GWO (HPSGWO) to solve the reliability-redundancy optimization problem has been proposed. The HPSGWO fuses the Particle Swarm Optimization's (PSO) exploitation ability with the grey wolf optimizer's (GWO) exploration ability. The comparison of results with prior best results of PSO and GWO for the four benchmarks of reliability redundancy allocation problem demonstrates the HPSGWO as a productive enhancement strategy since it got promising answers than other metaheuristic algorithms.

Roads have always been the main source of transportation all over the world. Easy accessibility and more safety are the most important features of road transportation. Improvements in these areas are constantly required and invited. Solar road studs are one of the remarkable improvements in road safety. Solar road studs use solar energy, which is the most sustainable and pollution-free source of energy that provides reliable power supplies and fuel diversification. Solar road studs are flashing solar cell-powered LED lighting devices used in road construction to delineate road edges and centerlines. This research work is dedicated to evaluating the reliability measures which include availability, mean time to failure (MTTF), cost analysis, and sensitivity analysis with their graphical representation by using the Markov process. Along with reliability assessment, Particle Swarm Optimization (PSO) technique is applied to optimize the cost of the system.

Reliability allocation for components, redundancy allocation, and reliability redundancy allocation are of great significance for system reliability designing. Generally, standby redundancy gives higher reliability for any system than active redundancy, but standby redundancy has more complex modeling than active redundancy. Cold standby strategy is one of the most consistently applied procedures to accomplish high‐reliability necessity, where backups are performed to guarantee that a backup part can take control over the undertaking successfully when the currently working fizzles. Considering the fact that components may fail during the switching process from standby to active, the impact of an imperfect switch is also applied in the system. In this work, a new hybrid GWO‐PSO(HPSGWO) algorithm, based on Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO), is presented to solve the cold‐standby reliability redundancy allocation problem (RRAP). The RRAP is a popular mixed integer nonlinear programming issue in a system plan that necessitates that the reliability target is set to fulfill the resource utilization requirement. Four contextual analyses are examined to feature the applicability of the proposed algorithm. The outcomes are compared with those obtained from PSO and GWO.

Reliability Redundancy Allocation Problem (RRAP) plays a vital role in reliability improvement and designing of any system which depends on the arrangement of components i.e., series, parallel, or complex, reliability of the components, and redundancy allocation for the components. In this work, a Fire Extinguisher Drone (FED) is considered for RRAP. The FEDs are very valuable for firefighters in tackling emergencies in non-reachable areas. To maximize the reliability of FED a non-linear mixed integer programming problem is formulated and optimized using the Hybrid Particle Swarm Grey Wolf Optimizer (HPSGWO). This metaheuristic fuses the Particle Swarm Optimization’s (PSO) exploitation ability with the grey wolf optimizer’s (GWO) exploration ability. With constraints such as cost, weight, and volume for the system, different levels of redundancies are applied to get the best redundancy allocation that maximizes the reliability of FED. Also, the results are of HPSGWO for each allocation are compared with the results of GWO, which clearly explains the superiority of the HPSGWO over GWO as well as PSO.

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