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dc.contributor.authorZaheer H.-
dc.contributor.authorPant M.-
dc.contributor.authorKumar S.-
dc.contributor.authorMonakhov O.-
dc.contributor.authorMonakhova E.-
dc.contributor.authorDeep, K.-
dc.date.accessioned2020-10-15T12:04:34Z-
dc.date.available2020-10-15T12:04:34Z-
dc.date.issued2017-
dc.identifier.citationInternational Journal of Systems Assurance Engineering and Management (2017), 8(): 2170-2183-
dc.identifier.issn9756809-
dc.identifier.urihttps://doi.org/10.1007/s13198-014-0322-6-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/9858-
dc.description.abstractPast few decades have witnessed the growth and development of different optimization techniques that can be applied for solving complex problems that are otherwise difficult to solve by traditional methods. Differential evolution (DE) has attained the reputation of a powerful optimization technique that can be used for solving a wide range of problems. In DE, mutation is the most important operator as it helps in generating a new solution vector. In this paper we propose an additional mutation strategy for DE. The suggested strategy is named DE/rand-to-best-best/2. It makes use of an additional parameter called guiding force parameter K, which takes a value between (0,1) besides using the scaling factor F, which has a fixed value. DE/rand-to-best-best/2 makes use of two difference vectors, where the difference is taken from the best solution vector. One vector difference will be produced with a randomly generated mutation factor K (0,1). Advantage of this strategy is, it will add a different vector to the old one and search space will increase with a random factor. Result shows that this strategy performs well in comparison to other mutation strategies of DE. © 2015, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.-
dc.language.isoen_US-
dc.publisherSpringer India-
dc.relation.ispartofInternational Journal of Systems Assurance Engineering and Management-
dc.subjectAmplifying factor-
dc.subjectDifferential evolution-
dc.subjectMutation-
dc.subjectMutation strategies-
dc.subjectOptimization-
dc.titleA new guiding force strategy for differential evolution-
dc.typeArticle-
dc.scopusid56594969400-
dc.scopusid23467551900-
dc.scopusid56927077600-
dc.scopusid6602142714-
dc.scopusid6603368033-
dc.scopusid8561208900-
dc.affiliationZaheer, H., Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Saharanpur, India-
dc.affiliationPant, M., Department of Applied Science and Engineering, Indian Institute of Technology Roorkee, Saharanpur, India-
dc.affiliationKumar, S., Department of Computer Science, LPU, Jalandhar, India-
dc.affiliationMonakhov, O., Computer Systems Department, Institute of Computational Mathematics and Mathematical Geophysics, Novosibirsk, Russian Federation-
dc.affiliationMonakhova, E., Computer Systems Department, Institute of Computational Mathematics and Mathematical Geophysics, Novosibirsk, Russian Federation-
dc.affiliationDeep, K., Mathematics Department, Indian Institute of Technology Roorkee, Roorkee, India-
dc.description.fundingAcknowledgments The reported study was partially supported by DST, research project No. INT/RFBR/P-164, and RFBR, research project No. 14-01-92694 IND-a-
dc.description.correspondingauthorZaheer, H.; Department of Applied Science and Engineering, Indian Institute of Technology RoorkeeIndia; email: hirazaheeriitr@gmail.com-
Appears in Collections:Journal Publications [MA]

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