Hybridization of gradient descent algorithms with dynamic tunneling methods for global optimization

An algorithm based on gradient descent techniques with dynamic tunneling methods for global optimization is proposed. The proposed algorithm consists of gradient descent fur local search and a direct search scheme, based on dynamic tunneling technique, for repelling away from local minimum to find t...

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Main Authors: RoyChowdhury, Pinaki, Chansarkar, R. A., Singh, Y. P.
Format: Article
Language:English
Published: IEEE Systems, Man, and Cybernetics Society 2000
Subjects:
Online Access:http://shdl.mmu.edu.my/2714/
http://shdl.mmu.edu.my/2714/1/Hybridization%20of%20gradient%20descent%20algorithms%20with%20dynamic%20tunneling%20methods%20for%20global%20optimization.pdf
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author RoyChowdhury, Pinaki
Chansarkar, R. A.
Singh, Y. P.
author_facet RoyChowdhury, Pinaki
Chansarkar, R. A.
Singh, Y. P.
author_sort RoyChowdhury, Pinaki
building MMU Institutional Repository
collection Online Access
description An algorithm based on gradient descent techniques with dynamic tunneling methods for global optimization is proposed. The proposed algorithm consists of gradient descent fur local search and a direct search scheme, based on dynamic tunneling technique, for repelling away from local minimum to find the point of next local descent. This search process applied repeatedly finds the global minimum of an objective function. The convergence properties of the proposed algorithm is validated experimentally on benchmark problems. A comparative computational results confirm the importance of dynamic tunneling in gradient descent techniques.
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spelling mmu-27142013-10-29T07:39:55Z http://shdl.mmu.edu.my/2714/ Hybridization of gradient descent algorithms with dynamic tunneling methods for global optimization RoyChowdhury, Pinaki Chansarkar, R. A. Singh, Y. P. QA75.5-76.95 Electronic computers. Computer science An algorithm based on gradient descent techniques with dynamic tunneling methods for global optimization is proposed. The proposed algorithm consists of gradient descent fur local search and a direct search scheme, based on dynamic tunneling technique, for repelling away from local minimum to find the point of next local descent. This search process applied repeatedly finds the global minimum of an objective function. The convergence properties of the proposed algorithm is validated experimentally on benchmark problems. A comparative computational results confirm the importance of dynamic tunneling in gradient descent techniques. IEEE Systems, Man, and Cybernetics Society 2000-05 Article NonPeerReviewed text en http://shdl.mmu.edu.my/2714/1/Hybridization%20of%20gradient%20descent%20algorithms%20with%20dynamic%20tunneling%20methods%20for%20global%20optimization.pdf RoyChowdhury, Pinaki and Chansarkar, R. A. and Singh, Y. P. (2000) Hybridization of gradient descent algorithms with dynamic tunneling methods for global optimization. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 30 (3). pp. 384-390. ISSN 1083-4427 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=844362 10.1109/3468.844362 10.1109/3468.844362 10.1109/3468.844362
spellingShingle QA75.5-76.95 Electronic computers. Computer science
RoyChowdhury, Pinaki
Chansarkar, R. A.
Singh, Y. P.
Hybridization of gradient descent algorithms with dynamic tunneling methods for global optimization
title Hybridization of gradient descent algorithms with dynamic tunneling methods for global optimization
title_full Hybridization of gradient descent algorithms with dynamic tunneling methods for global optimization
title_fullStr Hybridization of gradient descent algorithms with dynamic tunneling methods for global optimization
title_full_unstemmed Hybridization of gradient descent algorithms with dynamic tunneling methods for global optimization
title_short Hybridization of gradient descent algorithms with dynamic tunneling methods for global optimization
title_sort hybridization of gradient descent algorithms with dynamic tunneling methods for global optimization
topic QA75.5-76.95 Electronic computers. Computer science
url http://shdl.mmu.edu.my/2714/
http://shdl.mmu.edu.my/2714/
http://shdl.mmu.edu.my/2714/
http://shdl.mmu.edu.my/2714/1/Hybridization%20of%20gradient%20descent%20algorithms%20with%20dynamic%20tunneling%20methods%20for%20global%20optimization.pdf