Journal of South China University of Technology(Natural Science) >

2020 , Vol. 48 >Issue 10: 48 - 55

DOI: https://doi.org/10.12141/j.issn.1000-565X.190824

Architecture & Civil Engineering

Application of Improved Particle Swarm Mutation Algorithm to Building Energy-Saving Optimization

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  • 1． School of Architecture，Tianjin University，Tianjin 300072，China; 2． Tianjin Key Laboratory of Architectural Physics and Environmental Technology，Tianjin University，Tianjin 300072，China; 3． International Engineering Institute，Tianjin University，Tianjin 300072，China; 4． China Construction Engineering Design Group Corporation Limited，Beijing 100037，China
刘刚(1977-)，男，教授，博士生导师，主要从事绿色建筑技术研究。

Received date: 2019-11-11

  Revised date: 2020-05-08

  Online published: 2020-09-14

Supported by

Supported by the National Key R＆D Program of China (2016YFC0700200) and the National Natural Science Foundation of China (51628803)

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Abstract

When particle swarm optimization algorithm is used in the building energy-saving optimization，it is easy to converge untimely in the local optima due to the multi-peak characteristic of the building energy model． The combination of the distributed mutation operator and particle swarm algorithm can provide a solution to this pro-blem． In this paper，4 existing particle swarm mutation algorithms were studied． Aiming at the problem of low effects of optimization，the specific operations of the mutations were improved，and the effectiveness of 4 improved algo-rithms was verified by test functions． Then，they were applied to a building energy-saving optimization case． Experi-mental results show that，as compared with the existing particle swarm mutation algorithms，the improved algorithm can decrease the average value of object function by 1. 3% at least and increase the convergence rate by over 3 times，which means that the optimization effect is obviously improved． Thus，it is concluded that the improved al-gorithms is effective and applicable in general building energy-saving optimization．

Key words： building energy-saving optimization; particle swarm mutation algorithm; algorithm improvement; computer-aided architecture design

Cite this article

LIU Gang, WANG Mo, DONG Weixing, et al . Application of Improved Particle Swarm Mutation Algorithm to Building Energy-Saving Optimization[J]. Journal of South China University of Technology(Natural Science), 2020 , 48(10) : 48 -55 . DOI: 10.12141/j.issn.1000-565X.190824

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