Grey wolf optimizer and hybrid PSO-GWO for reliability optimization and redundancy allocation problem

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.     

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

To ensure the safety of nuclear power plants (NPPs), nuclear regulatory agencies set technical specifications (TSs). TSs define the safety‐related operational measures and specify essential requirements and set specific limitations that is necessarily be followed by a nuclear industry to meet the requirements for the safety of an NPP. One of the important bases for the setting of TSs is the estimates of the availability and reliability of various systems and costs associated with an NPP. In this work, authors have presented a framework based upon a hodiernal nature‐inspired metaheuristic called multiobjective gray wolf optimizer (MOGWO) algorithm, which mimic the hierarchal and hunting behavior of gray wolves (Canis lupus), for technical specifications optimization of residual heat removal system (RHRS) of an NPP safety system. The efficiency of MOGWO in optimizing the TSs is demonstrated by comparing its results with a very popular swarm‐based optimization technique named multiobjective particle swarm optimization (MOPSO).     

System Reliability Optimization Using Gray Wolf Optimizer Algorithm

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. Copyright © 2016 John Wiley & Sons, Ltd.     

Risk assessment for sustainability in e-waste recycling in circular economy

Clean Technologies and Environmental Policy - E-waste is a hazardous concept for human health, environment and businesses, and therefore, risk assessment is essential to eliminate or reduce the...