System Reliability Optimization Using Gray Wolf Optimizer Algorithm

• Published in: Quality and reliability engineering international Vol. 33; no. 7; pp. 1327 - 1335
• Main Authors: Kumar, Anuj, Pant, Sangeeta, Ram, Mangey
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.11.2017
• Subjects: Algorithms; Ant colony optimization; Computer simulation; gray wolf optimizer; Heuristic methods; Hunting; metaheuristic approaches; Nonlinear programming; optimization; Particle swarm optimization; reliability optimization; Search algorithms; Simulated annealing; System reliability; Wolves
• ISSN: 0748-8017; 1099-1638
• DOI: 10.1002/qre.2107

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.