A swarm intelligent approach for multi-objective optimization of compact heat exchangers
Design optimization of heat exchangers is a very complicated task that has been traditionally carried out based on a trial-and-error procedure. To overcome the difficulties of the conventional design approaches especially when a large number of variables, constraints and objectives are involved, a n...
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my.uniten.dspace-113502018-12-14T07:07:42Z A swarm intelligent approach for multi-objective optimization of compact heat exchangers Yousefi, M. Martins Ferreira, R.P. Darus, A.N. Design optimization of heat exchangers is a very complicated task that has been traditionally carried out based on a trial-and-error procedure. To overcome the difficulties of the conventional design approaches especially when a large number of variables, constraints and objectives are involved, a new method based on a well-established evolutionary algorithm, particle swarm optimization, weighted sum approach and a novel constraint handling strategy is presented in this study. Since the conventional constraint handling strategies are not effective and easy-to-implement in multi-objective algorithms, a novel feasibility-based ranking strategy is introduced which is both extremely user-friendly and effective. A case study from industry has been investigated to illustrate the performance of the presented approach. The results show that the proposed algorithm can find the near pareto-optimal with higher accuracy when it is compared to conventional non-dominated sorting genetic algorithm II. Moreover, the difficulties of a trial-and-error process for setting the penalty parameters are solved in this algorithm. © IMechE 2015. 2018-12-14T02:42:53Z 2018-12-14T02:42:53Z 2017 Article 10.1177/0954408915581995 en |
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Design optimization of heat exchangers is a very complicated task that has been traditionally carried out based on a trial-and-error procedure. To overcome the difficulties of the conventional design approaches especially when a large number of variables, constraints and objectives are involved, a new method based on a well-established evolutionary algorithm, particle swarm optimization, weighted sum approach and a novel constraint handling strategy is presented in this study. Since the conventional constraint handling strategies are not effective and easy-to-implement in multi-objective algorithms, a novel feasibility-based ranking strategy is introduced which is both extremely user-friendly and effective. A case study from industry has been investigated to illustrate the performance of the presented approach. The results show that the proposed algorithm can find the near pareto-optimal with higher accuracy when it is compared to conventional non-dominated sorting genetic algorithm II. Moreover, the difficulties of a trial-and-error process for setting the penalty parameters are solved in this algorithm. © IMechE 2015. |
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Article |
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Yousefi, M. Martins Ferreira, R.P. Darus, A.N. |
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Yousefi, M. Martins Ferreira, R.P. Darus, A.N. A swarm intelligent approach for multi-objective optimization of compact heat exchangers |
author_facet |
Yousefi, M. Martins Ferreira, R.P. Darus, A.N. |
author_sort |
Yousefi, M. |
title |
A swarm intelligent approach for multi-objective optimization of compact heat exchangers |
title_short |
A swarm intelligent approach for multi-objective optimization of compact heat exchangers |
title_full |
A swarm intelligent approach for multi-objective optimization of compact heat exchangers |
title_fullStr |
A swarm intelligent approach for multi-objective optimization of compact heat exchangers |
title_full_unstemmed |
A swarm intelligent approach for multi-objective optimization of compact heat exchangers |
title_sort |
swarm intelligent approach for multi-objective optimization of compact heat exchangers |
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2018 |
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1644495185119281152 |
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13.159267 |