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System ReliabilityOptimization UsingGray Wolf OptimizerAlgorithm Anuj KumarSangeeta PantMangey Ram2016 год

System Reliability Optimization Using Gray Wolf Optimizer Algorithm
статья из журнала

Страница публикацииПубликация в OpenAlex
Аннотация: 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.
Год издания: 2016
Авторы: Anuj Kumar, Sangeeta Pant, Mangey Ram
Издательство: Wiley
Источник: Quality and Reliability Engineering International
Ключевые слова: Metaheuristic Optimization Algorithms Research, Reliability and Maintenance Optimization, Advanced Multi-Objective Optimization Algorithms