Journal of South China University of Technology (Natural Science Edition) ›› 2020, Vol. 48 ›› Issue (10): 48-55.doi: 10.12141/j.issn.1000-565X.190824

• Architecture & Civil Engineering • Previous Articles     Next Articles

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

LIU Gang1,2 WANG Mo2,3 DONG Weixing4 HUANG Wenlong4   

  1. 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
  • Received:2019-11-11 Revised:2020-05-08 Online:2020-10-25 Published:2020-09-14
  • Contact: 刘刚(1977-)，男，教授，博士生导师，主要从事绿色建筑技术研究。 E-mail:lglgmike@163.com
  • About author:刘刚(1977-)，男，教授，博士生导师，主要从事绿色建筑技术研究。
  • 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