http://repository.iitr.ac.in/handle/123456789/18958
DC Field | Value | Language |
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dc.contributor.author | Singh A. | - |
dc.contributor.author | Deep, K. | - |
dc.contributor.editor | Bansal J.C. | - |
dc.contributor.editor | Nagar A. | - |
dc.contributor.editor | Ojha A.K. | - |
dc.contributor.editor | Das K.N. | - |
dc.contributor.editor | Deep K. | - |
dc.date.accessioned | 2020-12-03T06:29:35Z | - |
dc.date.available | 2020-12-03T06:29:35Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Proceedings of Advances in Intelligent Systems and Computing, (2019), 185- 197 | - |
dc.identifier.isbn | 9.79E+12 | - |
dc.identifier.issn | 21945357 | - |
dc.identifier.uri | https://doi.org/10.1007/978-981-13-1592-3_14 | - |
dc.identifier.uri | http://repository.iitr.ac.in/handle/123456789/18958 | - |
dc.description.abstract | Gravitational Search Algorithm (GSA) is a memory-less, nature-inspired algorithm for nonlinear continuous optimization problems. In Singh et al. (a new Improved Gravitational Search Algorithm for function optimization using a novel “best-so-far†update mechanism. IEEE, pp. 35–39 (2015) [21]), Singh and Deep proposed an Improved GSA using best-so-far mechanism. In this paper, the problem of 3D reconstruction is modelled as a nonlinear optimization problem. GSA and Improved GSA are used to solve three reconstruction problems. Based on the several computational experiments and analysis, it is concluded that the performance of improved GSA is better than original GSA in terms of convergence and solution quality. © Springer Nature Singapore Pte Ltd. 2019. | - |
dc.language.iso | en_US | - |
dc.publisher | Springer Verlag | - |
dc.relation.ispartof | Proceedings of Advances in Intelligent Systems and Computing | - |
dc.subject | Continuous function optimization | - |
dc.subject | Gravitational search algorithm | - |
dc.subject | Heuristic technique | - |
dc.subject | NURBS | - |
dc.subject | Reconstruction | - |
dc.subject | Heuristic methods | - |
dc.subject | Image reconstruction | - |
dc.subject | Learning algorithms | - |
dc.subject | Nonlinear programming | - |
dc.subject | Soft computing | - |
dc.subject | Continuous function optimization | - |
dc.subject | Continuous optimization problems | - |
dc.subject | Gravitational search algorithm (GSA) | - |
dc.subject | Gravitational search algorithms | - |
dc.subject | Heuristic techniques | - |
dc.subject | Nature inspired algorithms | - |
dc.subject | Non-linear optimization problems | - |
dc.subject | NURBS | - |
dc.subject | Problem solving | - |
dc.title | Use of improved gravitational search algorithm for 3D reconstruction of space curves using NURBS | - |
dc.type | Conference Paper | - |
dc.scopusid | 57211750941 | - |
dc.scopusid | 8561208900 | - |
dc.affiliation | Singh, A., Department of Mathematics, Janki Devi Memorial College, University of Delhi, New Delhi, 110060, India | - |
dc.affiliation | Deep, K., Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttrakhand 247667, India | - |
dc.description.correspondingauthor | Singh, A.; Department of Mathematics, Janki Devi Memorial College, University of DelhiIndia; email: amarjeetiitr@gmail.com | - |
dc.identifier.conferencedetails | 7th International Conference on Soft Computing for Problem Solving, SocProS 2017, 23-24 December 2017 | - |
Appears in Collections: | Conference Publications [MA] |
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