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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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
2018
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| Summary: | 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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