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 (N...
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| Published in | Quality and reliability engineering international Vol. 33; no. 7; pp. 1327 - 1335 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Bognor Regis
Wiley Subscription Services, Inc
01.11.2017
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0748-8017 1099-1638 |
| DOI | 10.1002/qre.2107 |
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| 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. Copyright © 2016 John Wiley & Sons, Ltd. |
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| AbstractList | 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. |
| Author | Ram, Mangey Pant, Sangeeta Kumar, Anuj |
| Author_xml | – sequence: 1 givenname: Anuj surname: Kumar fullname: Kumar, Anuj organization: University of Petroleum & Energy Studies – sequence: 2 givenname: Sangeeta orcidid: 0000-0001-6531-1561 surname: Pant fullname: Pant, Sangeeta organization: University of Petroleum & Energy Studies – sequence: 3 givenname: Mangey orcidid: 0000-0002-8221-092X surname: Ram fullname: Ram, Mangey email: drmrswami@yahoo.com organization: Graphic Era University |
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| SubjectTerms | 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 |
| Title | System Reliability Optimization Using Gray Wolf Optimizer Algorithm |
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