A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers
This paper presents a robust, efficient and parameter-setting-free evolutionary approach for the optimal design of compact heat exchangers. A learning automata based particle swarm optimization (LAPSO) is developed for optimization task. Seven design parameters, including discreet and continuous one...
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my.utm.465302017-09-12T07:45:21Z http://eprints.utm.my/id/eprint/46530/ A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers Yousefi, Moslem Enayatifar, Rasul Darus, Amer Nordin Abdullah, Hanan QC Physics This paper presents a robust, efficient and parameter-setting-free evolutionary approach for the optimal design of compact heat exchangers. A learning automata based particle swarm optimization (LAPSO) is developed for optimization task. Seven design parameters, including discreet and continuous ones, are considered as optimization variables. To make the constraint handling straightforward, a self-adaptive penalty function method is employed. The efficiency and the accuracy of the proposed method are demonstrated through two illustrative examples that include three objectives, namely minimum total annual cost, minimum weight and minimum number of entropy generation units. Numerical results indicate that the presented approach generates the optimum configuration with higher accuracy and a higher success rate when compared with genetic algorithms (GAs) and particle swarm optimization (PSO). 2012 Article PeerReviewed Yousefi, Moslem and Enayatifar, Rasul and Darus, Amer Nordin and Abdullah, Hanan (2012) A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers. International Communications in Heat and Mass Transfer, 39 (10). pp. 1605-1615. ISSN 0735-1933 http://dx.doi.org/10.1016/j.icheatmasstransfer.2012.10.002 |
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QC Physics Yousefi, Moslem Enayatifar, Rasul Darus, Amer Nordin Abdullah, Hanan A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers |
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This paper presents a robust, efficient and parameter-setting-free evolutionary approach for the optimal design of compact heat exchangers. A learning automata based particle swarm optimization (LAPSO) is developed for optimization task. Seven design parameters, including discreet and continuous ones, are considered as optimization variables. To make the constraint handling straightforward, a self-adaptive penalty function method is employed. The efficiency and the accuracy of the proposed method are demonstrated through two illustrative examples that include three objectives, namely minimum total annual cost, minimum weight and minimum number of entropy generation units. Numerical results indicate that the presented approach generates the optimum configuration with higher accuracy and a higher success rate when compared with genetic algorithms (GAs) and particle swarm optimization (PSO). |
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Article |
author |
Yousefi, Moslem Enayatifar, Rasul Darus, Amer Nordin Abdullah, Hanan |
author_facet |
Yousefi, Moslem Enayatifar, Rasul Darus, Amer Nordin Abdullah, Hanan |
author_sort |
Yousefi, Moslem |
title |
A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers |
title_short |
A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers |
title_full |
A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers |
title_fullStr |
A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers |
title_full_unstemmed |
A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers |
title_sort |
robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers |
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2012 |
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http://eprints.utm.my/id/eprint/46530/ http://dx.doi.org/10.1016/j.icheatmasstransfer.2012.10.002 |
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1643652062553047040 |
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13.18916 |