Combining Interval Branch and Bound and Stochastic Search
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 b...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/861765 |
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| Summary: | 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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| ISSN: | 1085-3375 1687-0409 |