A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II

When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating a subgeneration, the utilization of existing samples is low and the number of individuals is high. Therefore, surrogate-based optimization has been developed, wh...
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Autor*in:	Yu Guo [verfasserIn] Yukun Wang [verfasserIn] Yi Cao [verfasserIn] Zhengwei Long [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	air supply optimization double-objective optimization surrogate-based optimization Kriging model genetic algorithm

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 13(2023), 10465, p 10465
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:10465, p 10465

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app131810465

Katalog-ID:	DOAJ093459696

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language	English
source	In Applied Sciences 13(2023), 10465, p 10465 volume:13 year:2023 number:10465, p 10465
sourceStr	In Applied Sciences 13(2023), 10465, p 10465 volume:13 year:2023 number:10465, p 10465
format_phy_str_mv	Article
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topic_facet	air supply optimization double-objective optimization surrogate-based optimization Kriging model genetic algorithm Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry
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container_title	Applied Sciences
authorswithroles_txt_mv	Yu Guo @@aut@@ Yukun Wang @@aut@@ Yi Cao @@aut@@ Zhengwei Long @@aut@@
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callnumber-first	T - Technology
author	Yu Guo
spellingShingle	Yu Guo misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc air supply optimization misc double-objective optimization misc surrogate-based optimization misc Kriging model misc genetic algorithm misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II air supply optimization double-objective optimization surrogate-based optimization Kriging model genetic algorithm
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc air supply optimization misc double-objective optimization misc surrogate-based optimization misc Kriging model misc genetic algorithm misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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topic_browse	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc air supply optimization misc double-objective optimization misc surrogate-based optimization misc Kriging model misc genetic algorithm misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II
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title_full	A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II
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title_sort	new optimization design method of multi-objective indoor air supply using the kriging model and nsga-ii
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title_auth	A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II
abstract	When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating a subgeneration, the utilization of existing samples is low and the number of individuals is high. Therefore, surrogate-based optimization has been developed, which greatly reduces the number of individuals in the subgeneration and the cost of optimization. In complex air supply scenarios, single-objective optimization results may not be comprehensive; therefore, this paper developed a double-objective air supply optimization method based on the Kriging surrogate model and Non-dominated Sorting Genetic Algorithms-II. And it proposed the infill criteria based on clustering to advance the Pareto Frontier. The method was validated with an inverse prediction case, and in particular, the problems when based on 3D steady-state simulations were analyzed. The results showed that the method can quickly achieve an approximate prediction of the boundary conditions (when predictions were made based on experimental data, the number of simulations was 82 and the average error was 6.8%). Finally, the method was used to optimize the air supply parameters of a dual-aisle, single-row cabin, with only 118 samples used in the optimization process. The Pareto set suggested that an airflow organization with dual circulation may be optimal.
abstractGer	When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating a subgeneration, the utilization of existing samples is low and the number of individuals is high. Therefore, surrogate-based optimization has been developed, which greatly reduces the number of individuals in the subgeneration and the cost of optimization. In complex air supply scenarios, single-objective optimization results may not be comprehensive; therefore, this paper developed a double-objective air supply optimization method based on the Kriging surrogate model and Non-dominated Sorting Genetic Algorithms-II. And it proposed the infill criteria based on clustering to advance the Pareto Frontier. The method was validated with an inverse prediction case, and in particular, the problems when based on 3D steady-state simulations were analyzed. The results showed that the method can quickly achieve an approximate prediction of the boundary conditions (when predictions were made based on experimental data, the number of simulations was 82 and the average error was 6.8%). Finally, the method was used to optimize the air supply parameters of a dual-aisle, single-row cabin, with only 118 samples used in the optimization process. The Pareto set suggested that an airflow organization with dual circulation may be optimal.
abstract_unstemmed	When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating a subgeneration, the utilization of existing samples is low and the number of individuals is high. Therefore, surrogate-based optimization has been developed, which greatly reduces the number of individuals in the subgeneration and the cost of optimization. In complex air supply scenarios, single-objective optimization results may not be comprehensive; therefore, this paper developed a double-objective air supply optimization method based on the Kriging surrogate model and Non-dominated Sorting Genetic Algorithms-II. And it proposed the infill criteria based on clustering to advance the Pareto Frontier. The method was validated with an inverse prediction case, and in particular, the problems when based on 3D steady-state simulations were analyzed. The results showed that the method can quickly achieve an approximate prediction of the boundary conditions (when predictions were made based on experimental data, the number of simulations was 82 and the average error was 6.8%). Finally, the method was used to optimize the air supply parameters of a dual-aisle, single-row cabin, with only 118 samples used in the optimization process. The Pareto set suggested that an airflow organization with dual circulation may be optimal.
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title_short	A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II
url	https://doi.org/10.3390/app131810465 https://doaj.org/article/778dad6117ff457f9ec5032422fe4041 https://www.mdpi.com/2076-3417/13/18/10465 https://doaj.org/toc/2076-3417
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A New Optimization Design Method of Multi-Objective Indoor Air Supply Using the Kriging Model and NSGA-II

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