Combining interval branch and bound and stochastic search
© 2014 Dhiranuch Bunnag. This paper presents global optimization algorithms that incorporate the idea of an interval branch and bound and the stochastic search algorithms. Two algorithms for unconstrained problems are proposed, the hybrid interval simulated annealing and the combined interval branch...
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th-cmuir.6653943832-388222015-06-16T07:54:19Z Combining interval branch and bound and stochastic search Bunnag,D. Applied Mathematics Analysis © 2014 Dhiranuch Bunnag. This paper presents global optimization algorithms that incorporate the idea of an interval branch and bound and the stochastic search algorithms. Two algorithms for unconstrained problems are proposed, the hybrid interval simulated annealing and the combined interval branch and bound and genetic algorithm. The numerical experiment shows better results compared to Hansen's algorithm and simulated annealing in terms of the storage, speed, and number of function evaluations. The convergence proof is described. Moreover, the idea of both algorithms suggests a structure for an integrated interval branch and bound and genetic algorithm for constrained problems in which the algorithm is described and tested. The aim is to capture one of the solutions with higher accuracy and lower cost. The results show better quality of the solutions with less number of function evaluations compared with the traditional GA. 2015-06-16T07:54:19Z 2015-06-16T07:54:19Z 2014-01-01 Article 10853375 2-s2.0-84915746612 10.1155/2014/861765 http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84915746612&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/38822 Hindawi Publishing Corporation |
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Applied Mathematics Analysis Bunnag,D. Combining interval branch and bound and stochastic search |
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© 2014 Dhiranuch Bunnag. This paper presents global optimization algorithms that incorporate the idea of an interval branch and bound and the stochastic search algorithms. Two algorithms for unconstrained problems are proposed, the hybrid interval simulated annealing and the combined interval branch and bound and genetic algorithm. The numerical experiment shows better results compared to Hansen's algorithm and simulated annealing in terms of the storage, speed, and number of function evaluations. The convergence proof is described. Moreover, the idea of both algorithms suggests a structure for an integrated interval branch and bound and genetic algorithm for constrained problems in which the algorithm is described and tested. The aim is to capture one of the solutions with higher accuracy and lower cost. The results show better quality of the solutions with less number of function evaluations compared with the traditional GA. |
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Bunnag,D. |
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Bunnag,D. |
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Bunnag,D. |
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Combining interval branch and bound and stochastic search |
title_short |
Combining interval branch and bound and stochastic search |
title_full |
Combining interval branch and bound and stochastic search |
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Combining interval branch and bound and stochastic search |
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Combining interval branch and bound and stochastic search |
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combining interval branch and bound and stochastic search |
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Hindawi Publishing Corporation |
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2015 |
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http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84915746612&origin=inward http://cmuir.cmu.ac.th/handle/6653943832/38822 |
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