A hybrid approach to constrained global optimization
In this paper, we propose a novel hybrid global optimization method to solve constrained optimization problems. An exact penalty function is first applied to approximate the original constrained optimization problem by a sequence of optimization problems with bound constraints. To solve each of thes...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Published: |
Elsevier BV
2016
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| Online Access: | http://purl.org/au-research/grants/arc/LP140100873 http://hdl.handle.net/20.500.11937/33410 |
| _version_ | 1848753939265093632 |
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| author | Liu, J. Zhang, S. Wu, Changzhi Liang, J. Wang, Xiangyu Teo, K. |
| author_facet | Liu, J. Zhang, S. Wu, Changzhi Liang, J. Wang, Xiangyu Teo, K. |
| author_sort | Liu, J. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, we propose a novel hybrid global optimization method to solve constrained optimization problems. An exact penalty function is first applied to approximate the original constrained optimization problem by a sequence of optimization problems with bound constraints. To solve each of these box constrained optimization problems, two hybrid methods are introduced, where two different strategies are used to combine limited memory BFGS (L-BFGS) with Greedy Diffusion Search (GDS). The convergence issue of the two hybrid methods is addressed. To evaluate the effectiveness of the proposed algorithm, 18 box constrained and 4 general constrained problems from the literature are tested. Numerical results obtained show that our proposed hybrid algorithm is more effective in obtaining more accurate solutions than those compared to. |
| first_indexed | 2025-11-14T08:32:29Z |
| format | Journal Article |
| id | curtin-20.500.11937-33410 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:32:29Z |
| publishDate | 2016 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-334102022-11-28T05:01:09Z A hybrid approach to constrained global optimization Liu, J. Zhang, S. Wu, Changzhi Liang, J. Wang, Xiangyu Teo, K. In this paper, we propose a novel hybrid global optimization method to solve constrained optimization problems. An exact penalty function is first applied to approximate the original constrained optimization problem by a sequence of optimization problems with bound constraints. To solve each of these box constrained optimization problems, two hybrid methods are introduced, where two different strategies are used to combine limited memory BFGS (L-BFGS) with Greedy Diffusion Search (GDS). The convergence issue of the two hybrid methods is addressed. To evaluate the effectiveness of the proposed algorithm, 18 box constrained and 4 general constrained problems from the literature are tested. Numerical results obtained show that our proposed hybrid algorithm is more effective in obtaining more accurate solutions than those compared to. 2016 Journal Article http://hdl.handle.net/20.500.11937/33410 10.1016/j.asoc.2016.05.021 http://purl.org/au-research/grants/arc/LP140100873 Elsevier BV fulltext |
| spellingShingle | Liu, J. Zhang, S. Wu, Changzhi Liang, J. Wang, Xiangyu Teo, K. A hybrid approach to constrained global optimization |
| title | A hybrid approach to constrained global optimization |
| title_full | A hybrid approach to constrained global optimization |
| title_fullStr | A hybrid approach to constrained global optimization |
| title_full_unstemmed | A hybrid approach to constrained global optimization |
| title_short | A hybrid approach to constrained global optimization |
| title_sort | hybrid approach to constrained global optimization |
| url | http://purl.org/au-research/grants/arc/LP140100873 http://hdl.handle.net/20.500.11937/33410 |