Genetic algorithm for building optimization - State-of-the-art survey

Tiejun Li; Guifang Shao; Wangda Zuo; Sen Huang

doi:10.1145/3055635.3056591

Genetic algorithm for building optimization - State-of-the-art survey

Tiejun Li, Guifang Shao, Wangda Zuo, Sen Huang

Architectural Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

48 Scopus citations

Abstract

Model-based building operation optimization can be used to reduce building energy consumption, so as to improve the indoor environment quality. Genetic Algorithm (GA) is one of the commonly used optimization algorithms for building applications. To provide readers up-to-date information, this paper attempts to summarize recent researches on building optimization with GA. Firstly, the principle of GA is introduced. Then, we summarize the literatures according to different categories, including applied system types and optimization objectives. We also provide some insights into the parameter setting and operator selection for GA. This review paper intends to give a better understanding and some future directions for building research community on how to apply GA for building energy optimization.

Original language	English (US)
Title of host publication	Proceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017
Publisher	Association for Computing Machinery
Pages	205-210
Number of pages	6
ISBN (Electronic)	9781450348171
DOIs	https://doi.org/10.1145/3055635.3056591
State	Published - Feb 24 2017
Event	9th International Conference on Machine Learning and Computing, ICMLC 2017 - Singapore, Singapore Duration: Feb 24 2017 → Feb 26 2017

Publication series

Name	ACM International Conference Proceeding Series
Volume	Part F128357

Conference

Conference	9th International Conference on Machine Learning and Computing, ICMLC 2017
Country/Territory	Singapore
City	Singapore
Period	2/24/17 → 2/26/17

All Science Journal Classification (ASJC) codes

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/3055635.3056591

Cite this

Li, T., Shao, G., Zuo, W., & Huang, S. (2017). Genetic algorithm for building optimization - State-of-the-art survey. In Proceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017 (pp. 205-210). (ACM International Conference Proceeding Series; Vol. Part F128357). Association for Computing Machinery. https://doi.org/10.1145/3055635.3056591

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title = "Genetic algorithm for building optimization - State-of-the-art survey",

abstract = "Model-based building operation optimization can be used to reduce building energy consumption, so as to improve the indoor environment quality. Genetic Algorithm (GA) is one of the commonly used optimization algorithms for building applications. To provide readers up-to-date information, this paper attempts to summarize recent researches on building optimization with GA. Firstly, the principle of GA is introduced. Then, we summarize the literatures according to different categories, including applied system types and optimization objectives. We also provide some insights into the parameter setting and operator selection for GA. This review paper intends to give a better understanding and some future directions for building research community on how to apply GA for building energy optimization.",

author = "Tiejun Li and Guifang Shao and Wangda Zuo and Sen Huang",

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year = "2017",

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doi = "10.1145/3055635.3056591",

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Li, T, Shao, G, Zuo, W & Huang, S 2017, Genetic algorithm for building optimization - State-of-the-art survey. in Proceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017. ACM International Conference Proceeding Series, vol. Part F128357, Association for Computing Machinery, pp. 205-210, 9th International Conference on Machine Learning and Computing, ICMLC 2017, Singapore, Singapore, 2/24/17. https://doi.org/10.1145/3055635.3056591

Genetic algorithm for building optimization - State-of-the-art survey. / Li, Tiejun; Shao, Guifang; Zuo, Wangda et al.
Proceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017. Association for Computing Machinery, 2017. p. 205-210 (ACM International Conference Proceeding Series; Vol. Part F128357).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - Model-based building operation optimization can be used to reduce building energy consumption, so as to improve the indoor environment quality. Genetic Algorithm (GA) is one of the commonly used optimization algorithms for building applications. To provide readers up-to-date information, this paper attempts to summarize recent researches on building optimization with GA. Firstly, the principle of GA is introduced. Then, we summarize the literatures according to different categories, including applied system types and optimization objectives. We also provide some insights into the parameter setting and operator selection for GA. This review paper intends to give a better understanding and some future directions for building research community on how to apply GA for building energy optimization.

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Genetic algorithm for building optimization - State-of-the-art survey

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

UN SDGs

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