System Reliability Optimization Using Gray Wolf Optimizer Algorithm

Abstract

For the past two decades, nature-inspired optimization algorithms have gained enormous popularity among the researchers. On the other hand, complex system reliability optimization problems, which are nonlinear programming problems in nature, are proved to be non-deterministic polynomial-time hard (NP-hard) from a computational point of view. In this work, few complex reliability optimization problems are solved by using a very recent nature-inspired metaheuristic called gray wolf optimizer (GWO) algorithm. GWO mimics the chasing, hunting, and the hierarchal behavior of gray wolves. The results obtained by GWO are compared with those of some recent and popular metaheuristic such as the cuckoo search algorithm, particle swarm optimization, ant colony optimization, and simulated annealing. This comparative study shows that the results obtained by GWO are either superior or competitive to the results that have been obtained by these well-known metaheuristic mentioned earlier.SA =simulated annealing; GA = genetic algorithm; TS = tabu search; PSO = particle swarm optimization; CSA = cuckoo search algorithm; DP = dynamic programming.A. KUMAR, S. PANT AND M. RAM

Gray wolf optimizer approach to the reliability‐cost optimization of residual heat removal system of a nuclear power plant safety system

“…The whole process of hunting can be subdivided into three broad categories. First one is tracking and chasing the prey, second one is encircling and harassing the prey till the movement stops, and finally, the third one is attacking towards the prey …”

“…Recently, Pant et al proposed a modified particle swarm optimization (PSO) for nonlinear optimization. Kumar et al applied single objective gray wolf optimizer (GWO) for reliability optimization of few complex systems.…”

Photovoltaic and Wind Turbine Integration Applying Cuckoo Search for Probabilistic Reliable Optimal Placement

“…(e.g., levels up to 4 m/s gives the same output power that is equal to zero, so it is considered as one level, the same for levels above 25 m/s and with levels from 14 m/s to 25 m/s where the output power is constant and equal to ) [18]. The generated wind speed samples can be transformed to wind turbine output power [17] using wind speed-power curve through Equation (5).…”

“…The probability (Prob PV ) of each level is calculated by counting the number of h/year for every level of irradiance and dividing it by total number of hours per year, as shown in Figure 4. The generated wind speed samples can be transformed to wind turbine output power [17] using wind speed-power curve through Equation (5).…”

“…A new nature-inspired algorithm is implemented to optimally solve the reliability problems. Generally, reliability optimization problems are very complex in nature and non-deterministic polynomial-time hard (NP-hard) from a computational point of view [5]. A differential search (DS) algorithm has been effectively applied to determine the optimal allocations of remote control switch (RCS) in a radial distribution feeder.…”