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A swarm intelligent approach for multi-objective optimization of compact heat exchangers

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dc.citedby9
dc.contributor.authorYousefi M.en_US
dc.contributor.authorYousefi M.en_US
dc.contributor.authorMartins Ferreira R.P.en_US
dc.contributor.authorDarus A.N.en_US
dc.contributor.authorid55247052200en_US
dc.contributor.authorid53985756300en_US
dc.contributor.authorid15623226000en_US
dc.contributor.authorid56584823100en_US
dc.date.accessioned2023-05-29T06:39:01Z
dc.date.available2023-05-29T06:39:01Z
dc.date.issued2017
dc.descriptionConstrained optimization; Genetic algorithms; Heat exchangers; Pareto principle; Particle swarm optimization (PSO); Compact heat exchanger; Constraint handling strategies; Constraints handling; Multi objective algorithm; Non-dominated sorting genetic algorithm - ii; Trial-and-error procedures; Trial-and-error process; Weighted sum approaches; Multiobjective optimizationen_US
dc.description.abstractDesign 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1177/0954408915581995
dc.identifier.epage171
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85015150471
dc.identifier.spage164
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85015150471&doi=10.1177%2f0954408915581995&partnerID=40&md5=8be104220c01ad6c33c885da94106c92
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23276
dc.identifier.volume231
dc.publisherSAGE Publications Ltden_US
dc.sourceScopus
dc.sourcetitleProceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering
dc.titleA swarm intelligent approach for multi-objective optimization of compact heat exchangersen_US
dc.typeArticleen_US
dspace.entity.typePublication
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