Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm

Andrew Kusiak, Guanglin Xu, Fan Tang

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


A data-driven approach for the optimization of a heating, ventilation, and air conditioning (HVAC) system in an office building is presented. A neural network (NN) algorithm is used to build a predictive model since it outperformed five other algorithms investigated in this paper. The NN-derived predictive model is then optimized with a strength multi-objective particle-swarm optimization (S-MOPSO) algorithm. The relationship between energy consumption and thermal comfort measured with temperature and humidity is discussed. The control settings derived from optimization of the model minimize energy consumption while maintaining thermal comfort at an acceptable level. The solutions derived by the S-MOPSO algorithm point to a large number of control alternatives for an HVAC system, representing a range of trade-offs between thermal comfort and energy consumption.

Original languageEnglish (US)
Pages (from-to)5935-5943
Number of pages9
Issue number10
StatePublished - Oct 2011

Bibliographical note

Funding Information:
This research has been supported by the Iowa Energy Center , Grant No. 08-01 .

Copyright 2017 Elsevier B.V., All rights reserved.


  • Evolutionary computation
  • HVAC
  • Neutral network
  • Optimization
  • Strength multi-objective particle-swarm algorithm


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