Solving chiller loading optimization problems using an improved teaching‐learning‐based optimization algorithm
Summary In this study, we present a novel teaching‐learning‐based optimization (TLBO) algorithm for solving the optimal chiller loading problem. The proposed algorithm uses a novel integer‐based encoding and decoding mechanism that is efficient and easy to implement. The teaching phase can improve t...
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| Veröffentlicht in: | Optimal control applications & methods Jg. 39; H. 1; S. 65 - 77 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Glasgow
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01.01.2018
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| ISSN: | 0143-2087, 1099-1514 |
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| Abstract | Summary
In this study, we present a novel teaching‐learning‐based optimization (TLBO) algorithm for solving the optimal chiller loading problem. The proposed algorithm uses a novel integer‐based encoding and decoding mechanism that is efficient and easy to implement. The teaching phase can improve the quality of learning process and thus enhance the exploitation ability. In addition, a well‐designed learning phase procedure is developed to enhance the learning process between one another in the population. A novel exploration and self‐learning procedures are embedded in the proposed TLBO algorithm, which can enhance the exploitation and exploration capabilities. The proposed algorithm is tested on several well‐known case studies and compared with several efficient algorithms. From the experimental comparisons, the efficient performance of the proposed TLBO is verified. |
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| AbstractList | Summary In this study, we present a novel teaching-learning-based optimization (TLBO) algorithm for solving the optimal chiller loading problem. The proposed algorithm uses a novel integer-based encoding and decoding mechanism that is efficient and easy to implement. The teaching phase can improve the quality of learning process and thus enhance the exploitation ability. In addition, a well-designed learning phase procedure is developed to enhance the learning process between one another in the population. A novel exploration and self-learning procedures are embedded in the proposed TLBO algorithm, which can enhance the exploitation and exploration capabilities. The proposed algorithm is tested on several well-known case studies and compared with several efficient algorithms. From the experimental comparisons, the efficient performance of the proposed TLBO is verified. Summary In this study, we present a novel teaching‐learning‐based optimization (TLBO) algorithm for solving the optimal chiller loading problem. The proposed algorithm uses a novel integer‐based encoding and decoding mechanism that is efficient and easy to implement. The teaching phase can improve the quality of learning process and thus enhance the exploitation ability. In addition, a well‐designed learning phase procedure is developed to enhance the learning process between one another in the population. A novel exploration and self‐learning procedures are embedded in the proposed TLBO algorithm, which can enhance the exploitation and exploration capabilities. The proposed algorithm is tested on several well‐known case studies and compared with several efficient algorithms. From the experimental comparisons, the efficient performance of the proposed TLBO is verified. In this study, we present a novel teaching‐learning‐based optimization (TLBO) algorithm for solving the optimal chiller loading problem. The proposed algorithm uses a novel integer‐based encoding and decoding mechanism that is efficient and easy to implement. The teaching phase can improve the quality of learning process and thus enhance the exploitation ability. In addition, a well‐designed learning phase procedure is developed to enhance the learning process between one another in the population. A novel exploration and self‐learning procedures are embedded in the proposed TLBO algorithm, which can enhance the exploitation and exploration capabilities. The proposed algorithm is tested on several well‐known case studies and compared with several efficient algorithms. From the experimental comparisons, the efficient performance of the proposed TLBO is verified. |
| Author | Sang, Hong‐yan Duan, Pei‐yong Jia, Bao‐xian Li, Jun‐qing Wang, Yong |
| Author_xml | – sequence: 1 givenname: Pei‐yong surname: Duan fullname: Duan, Pei‐yong organization: Shandong Normal University – sequence: 2 givenname: Jun‐qing orcidid: 0000-0002-3617-6708 surname: Li fullname: Li, Jun‐qing email: lijunqing@lcu‐cs.com organization: Northeastern University – sequence: 3 givenname: Yong surname: Wang fullname: Wang, Yong organization: Liaocheng University – sequence: 4 givenname: Hong‐yan surname: Sang fullname: Sang, Hong‐yan organization: Liaocheng University – sequence: 5 givenname: Bao‐xian surname: Jia fullname: Jia, Bao‐xian organization: Liaocheng University |
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| Cites_doi | 10.1016/j.enbuild.2012.11.030 10.1016/j.apenergy.2009.05.004 10.1109/TASE.2015.2425404 10.1016/j.enconman.2008.08.036 10.1016/j.applthermaleng.2005.02.010 10.1109/TPDS.2015.2401003 10.1016/j.enbuild.2008.06.010 10.1080/00207543.2015.1120900 10.1016/j.applthermaleng.2011.02.030 10.1016/j.energy.2015.12.030 10.1016/j.energy.2012.10.058 10.1016/j.ins.2011.08.006 10.1109/TIFS.2016.2596138 10.1016/j.energy.2005.10.018 10.1016/j.ins.2014.10.009 10.1016/j.epsr.2003.10.012 10.1016/j.engappai.2012.02.016 10.1587/transcom.E98.B.190 10.1016/j.enconman.2004.10.012 10.3390/pr1030312 10.1016/j.energy.2014.07.060 10.1109/CC.2016.7559082 10.1016/j.engappai.2014.09.015 10.1016/j.enbuild.2004.06.002 10.1016/j.enbuild.2009.10.009 10.1109/TCYB.2015.2444383 10.23919/ECC.2013.6669583 10.1109/TIFS.2016.2590944 10.1016/j.automatica.2015.09.013 |
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| References | 2012; 183 2015; 37 2013; 26 2006; 31 2013; 1 2015; E98.B 2004; 69 2016; 54 2011; 31 2016; 95 2016; 13 2005; 46 2009; 29 2016; 11 2005; 25 2010; 87 2010; 42 2013; 59 2015; 27 2009; 50 2015; 62 2013; 50 2011; 43 2017 2016 2015; 316 2013 2005; 37 2008; 40 2016; 46 2014; 75 Chang YC (e_1_2_8_19_1) 2006; 31 Powell KM (e_1_2_8_8_1) 2013; 50 e_1_2_8_24_1 Coelho LS (e_1_2_8_2_1) 2014; 75 e_1_2_8_26_1 Fu Z (e_1_2_8_15_1) 2016 Lee WS (e_1_2_8_17_1) 2009; 29 e_1_2_8_5_1 e_1_2_8_4_1 Lee WS (e_1_2_8_22_1) 2011; 43 Chang YC (e_1_2_8_21_1) 2010; 87 e_1_2_8_6_1 e_1_2_8_9_1 Chang YC (e_1_2_8_20_1) 2009; 50 e_1_2_8_23_1 Xue Y (e_1_2_8_32_1) 2017 Shen JN (e_1_2_8_28_1) 2016; 54 Chang YC (e_1_2_8_35_1) 2005; 25 Li JQ (e_1_2_8_30_1) 2017 e_1_2_8_18_1 e_1_2_8_14_1 e_1_2_8_16_1 Platt G (e_1_2_8_3_1) 2010; 42 Chang YC (e_1_2_8_7_1) 2005; 46 Qu Z (e_1_2_8_13_1) 2016; 13 Li J (e_1_2_8_31_1) 2015; 316 e_1_2_8_10_1 Li JQ (e_1_2_8_29_1) 2016; 46 e_1_2_8_11_1 Li JQ (e_1_2_8_27_1) 2015; 37 e_1_2_8_34_1 e_1_2_8_12_1 Rao RV (e_1_2_8_25_1) 2016 e_1_2_8_33_1 Geem ZW (e_1_2_8_36_1) 2011; 31 |
| References_xml | – volume: 59 start-page: 273 year: 2013 end-page: 278 article-title: Improved firefly algorithm approach applied to chiller loading for energy conservation publication-title: Energ Buildings – volume: 1 start-page: 312 issue: 3 year: 2013 end-page: 329 article-title: Improved large‐scale process cooling operation through energy optimization publication-title: Processes – volume: E98.B start-page: 190 issue: 1 year: 2015 end-page: 200 article-title: Achieving efficient cloud search services: multi‐keyword ranked search over encrypted cloud data supporting parallel computing publication-title: IEICE Transactions on Communications – volume: 42 start-page: 412 issue: 4 year: 2010 end-page: 421 article-title: Adaptive HVAC zone modeling for sustainable buildings publication-title: Energ Buildings – volume: 316 start-page: 487 issue: 20 year: 2015 end-page: 502 article-title: Solving the large‐scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm publication-title: Inform Sci – volume: 75 start-page: 237 year: 2014 end-page: 243 article-title: Optimal chiller loading for energy conservation using a new differential cuckoo search approach publication-title: Energy – volume: 37 start-page: 147 issue: 2 year: 2005 end-page: 155 article-title: Optimal chiller loading by genetic algorithm for reducing energy consumption publication-title: Energ Buildings – volume: 25 start-page: 2800 issue: 17 year: 2005 end-page: 2815 article-title: Genetic algorithm based optimal chiller loading for energy conservation publication-title: Appl Therm Eng – volume: 46 start-page: 2158 year: 2005 end-page: 2172 article-title: Optimal chiller sequencing by branch and bound method for saving energy publication-title: Energ Conver Manage – volume: 50 start-page: 445 year: 2013 end-page: 453 article-title: Optimal chiller loading in a district cooling system with thermal energy storage publication-title: Energy – year: 2017 article-title: A self‐adaptive articial bee colony algorithm based on global best for global optimization publication-title: Soft Computing – volume: 31 start-page: 1848 issue: 10 year: 2011 end-page: 1851 article-title: Solution quality improvement in chiller loading optimization publication-title: Appl Therm Eng – start-page: 4077 year: 2013 end-page: 4082 – volume: 29 start-page: 1730 issue: 8 year: 2009 end-page: 1734 article-title: Optimal chiller loading by particle swarm algorithm for reducing energy consumption publication-title: Appl Therm Eng – year: 2017 article-title: A hybrid artificial bee colony for optimizing a reverse logistics network system publication-title: Soft Computing – volume: 31 start-page: 1883 year: 2006 end-page: 1896 article-title: An innovative approach for demand side management‐optimal chiller loading by simulated annealing publication-title: Energy – volume: 183 start-page: 1 year: 2012 end-page: 15 article-title: Teaching‐learning‐based optimisation: an optimisation method for continuous non‐linear large scale problems publication-title: Inform Sci – volume: 26 start-page: 430 year: 2013 end-page: 445 article-title: Multi‐objective optimisation of two stage thermoelectric cooler using a modified teaching‐learning‐based optimisation algorithm publication-title: Eng Appl Artif Intel – volume: 62 start-page: 184 year: 2015 end-page: 192 article-title: Local optimization of dynamic programs with guaranteed satisfaction of path constraints publication-title: Automatica – volume: 37 start-page: 279 year: 2015 end-page: 292 article-title: A discrete teaching‐learning‐based optimisation algorithm for realistic flowshop rescheduling problems publication-title: Eng Appl Artif Intel – volume: 11 start-page: 2594 issue: 11 year: 2016 end-page: 2608 article-title: A privacy‐preserving and copy‐deterrence content‐based image retrieval scheme in cloud computing publication-title: IEEE Transactions on Information Forensics and Security – volume: 87 start-page: 1096 issue: 4 year: 2010 end-page: 1101 article-title: Economic dispatch of chiller plant by gradient method for saving energy publication-title: Appl Energy – year: 2016 article-title: Enabling semantic search based on conceptual graphs over encrypted outsourced data publication-title: IEEE Transactions on Services Computing – volume: 46 start-page: 1311 issue: 6 year: 2016 end-page: 1324 article-title: An improved artificial bee colony algorithm for solving hybrid flexible flowshop with dynamic operation skipping publication-title: IEEE transactions on cybernetics – volume: 27 start-page: 340 issue: 2 year: 2015 end-page: 352 article-title: A secure and dynamic multi‐keyword ranked search scheme over encrypted cloud data publication-title: IEEE Transactions on Parallel & Distributed Systems – volume: 54 start-page: 3622 issue: 12 year: 2016 end-page: 3639 article-title: A modified teaching‐learning‐based optimisation algorithm for bi‐objective re‐entrant hybrid flowshop scheduling publication-title: International Journal of Production Research – volume: 69 start-page: 221 year: 2004 end-page: 226 article-title: A novel energy conservation method‐optimal chiller loading publication-title: Electr Pow Syst Res – volume: 43 start-page: 599 issue: 2 year: 2011 end-page: 604 article-title: Optimal chiller loading by differential evolution algorithm for reducing energy consumption publication-title: Energ Buildings – start-page: 1 year: 2016 end-page: 23 article-title: Multi‐objective optimization of machining and micro‐machining processes using non‐dominated sorting teaching‐learning‐based optimization algorithm publication-title: J Intell Manuf – volume: 13 start-page: 932 issue: 2 year: 2016 end-page: 949 article-title: A hybrid fruit fly optimization algorithm for the realistic hybrid flowshop rescheduling problem in steelmaking systems publication-title: IEEE Transactions on Automation Science and Engineering – volume: 11 start-page: 2706 issue: 12 year: 2016 end-page: 2716 article-title: Toward efficient multi‐keyword fuzzy search over encrypted outsourced data with accuracy improvement publication-title: IEEE Transactions on Information Forensics and Security – volume: 50 start-page: 132 year: 2009 end-page: 139 article-title: Evolution strategy based optimal chiller loading for saving energy publication-title: Energ Conver Manage – volume: 40 start-page: 2177 issue: 12 year: 2008 end-page: 2187 article-title: A novel approach for optimal chiller loading using particle swarm optimization publication-title: Energ Buildings – volume: 95 start-page: 528 year: 2016 end-page: 541 article-title: Multi‐objective optimization of a Stirling heat engine using TS‐TLBO (tutorial training and self learning inspired teaching‐learning based optimization) algorithm publication-title: Energy – volume: 13 start-page: 108 issue: 7 year: 2016 end-page: 116 article-title: Multilevel pattern mining architecture for automatic network monitoring in heterogeneous wireless communication networks publication-title: China Communications – ident: e_1_2_8_18_1 doi: 10.1016/j.enbuild.2012.11.030 – volume: 87 start-page: 1096 issue: 4 year: 2010 ident: e_1_2_8_21_1 article-title: Economic dispatch of chiller plant by gradient method for saving energy publication-title: Appl Energy doi: 10.1016/j.apenergy.2009.05.004 – ident: e_1_2_8_33_1 doi: 10.1109/TASE.2015.2425404 – volume: 50 start-page: 132 year: 2009 ident: e_1_2_8_20_1 article-title: Evolution strategy based optimal chiller loading for saving energy publication-title: Energ Conver Manage doi: 10.1016/j.enconman.2008.08.036 – volume: 43 start-page: 599 issue: 2 year: 2011 ident: e_1_2_8_22_1 article-title: Optimal chiller loading by differential evolution algorithm for reducing energy consumption publication-title: Energ Buildings – volume: 25 start-page: 2800 issue: 17 year: 2005 ident: e_1_2_8_35_1 article-title: Genetic algorithm based optimal chiller loading for energy conservation publication-title: Appl Therm Eng doi: 10.1016/j.applthermaleng.2005.02.010 – ident: e_1_2_8_11_1 doi: 10.1109/TPDS.2015.2401003 – ident: e_1_2_8_5_1 doi: 10.1016/j.enbuild.2008.06.010 – volume: 54 start-page: 3622 issue: 12 year: 2016 ident: e_1_2_8_28_1 article-title: A modified teaching‐learning‐based optimisation algorithm for bi‐objective re‐entrant hybrid flowshop scheduling publication-title: International Journal of Production Research doi: 10.1080/00207543.2015.1120900 – volume: 31 start-page: 1848 issue: 10 year: 2011 ident: e_1_2_8_36_1 article-title: Solution quality improvement in chiller loading optimization publication-title: Appl Therm Eng doi: 10.1016/j.applthermaleng.2011.02.030 – ident: e_1_2_8_26_1 doi: 10.1016/j.energy.2015.12.030 – volume: 50 start-page: 445 year: 2013 ident: e_1_2_8_8_1 article-title: Optimal chiller loading in a district cooling system with thermal energy storage publication-title: Energy doi: 10.1016/j.energy.2012.10.058 – ident: e_1_2_8_23_1 doi: 10.1016/j.ins.2011.08.006 – ident: e_1_2_8_14_1 doi: 10.1109/TIFS.2016.2596138 – volume: 31 start-page: 1883 year: 2006 ident: e_1_2_8_19_1 article-title: An innovative approach for demand side management‐optimal chiller loading by simulated annealing publication-title: Energy doi: 10.1016/j.energy.2005.10.018 – volume: 29 start-page: 1730 issue: 8 year: 2009 ident: e_1_2_8_17_1 article-title: Optimal chiller loading by particle swarm algorithm for reducing energy consumption publication-title: Appl Therm Eng – volume: 316 start-page: 487 issue: 20 year: 2015 ident: e_1_2_8_31_1 article-title: Solving the large‐scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm publication-title: Inform Sci doi: 10.1016/j.ins.2014.10.009 – ident: e_1_2_8_6_1 doi: 10.1016/j.epsr.2003.10.012 – ident: e_1_2_8_24_1 doi: 10.1016/j.engappai.2012.02.016 – ident: e_1_2_8_16_1 doi: 10.1587/transcom.E98.B.190 – volume: 46 start-page: 2158 year: 2005 ident: e_1_2_8_7_1 article-title: Optimal chiller sequencing by branch and bound method for saving energy publication-title: Energ Conver Manage doi: 10.1016/j.enconman.2004.10.012 – start-page: 1 year: 2016 ident: e_1_2_8_25_1 article-title: Multi‐objective optimization of machining and micro‐machining processes using non‐dominated sorting teaching‐learning‐based optimization algorithm publication-title: J Intell Manuf – ident: e_1_2_8_9_1 doi: 10.3390/pr1030312 – volume: 75 start-page: 237 year: 2014 ident: e_1_2_8_2_1 article-title: Optimal chiller loading for energy conservation using a new differential cuckoo search approach publication-title: Energy doi: 10.1016/j.energy.2014.07.060 – year: 2017 ident: e_1_2_8_30_1 article-title: A hybrid artificial bee colony for optimizing a reverse logistics network system publication-title: Soft Computing – volume: 13 start-page: 108 issue: 7 year: 2016 ident: e_1_2_8_13_1 article-title: Multilevel pattern mining architecture for automatic network monitoring in heterogeneous wireless communication networks publication-title: China Communications doi: 10.1109/CC.2016.7559082 – volume: 37 start-page: 279 year: 2015 ident: e_1_2_8_27_1 article-title: A discrete teaching‐learning‐based optimisation algorithm for realistic flowshop rescheduling problems publication-title: Eng Appl Artif Intel doi: 10.1016/j.engappai.2014.09.015 – ident: e_1_2_8_4_1 doi: 10.1016/j.enbuild.2004.06.002 – year: 2017 ident: e_1_2_8_32_1 article-title: A self‐adaptive articial bee colony algorithm based on global best for global optimization publication-title: Soft Computing – volume: 42 start-page: 412 issue: 4 year: 2010 ident: e_1_2_8_3_1 article-title: Adaptive HVAC zone modeling for sustainable buildings publication-title: Energ Buildings doi: 10.1016/j.enbuild.2009.10.009 – volume: 46 start-page: 1311 issue: 6 year: 2016 ident: e_1_2_8_29_1 article-title: An improved artificial bee colony algorithm for solving hybrid flexible flowshop with dynamic operation skipping publication-title: IEEE transactions on cybernetics doi: 10.1109/TCYB.2015.2444383 – ident: e_1_2_8_10_1 doi: 10.23919/ECC.2013.6669583 – year: 2016 ident: e_1_2_8_15_1 article-title: Enabling semantic search based on conceptual graphs over encrypted outsourced data publication-title: IEEE Transactions on Services Computing – ident: e_1_2_8_12_1 doi: 10.1109/TIFS.2016.2590944 – ident: e_1_2_8_34_1 doi: 10.1016/j.automatica.2015.09.013 |
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In this study, we present a novel teaching‐learning‐based optimization (TLBO) algorithm for solving the optimal chiller loading problem. The proposed... In this study, we present a novel teaching‐learning‐based optimization (TLBO) algorithm for solving the optimal chiller loading problem. The proposed algorithm... Summary In this study, we present a novel teaching-learning-based optimization (TLBO) algorithm for solving the optimal chiller loading problem. The proposed... |
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| SubjectTerms | Algorithms Decoding energy conversation Exploitation Exploration Learning Machine learning optimal chiller loading Optimization teaching‐learning‐based optimization |
| Title | Solving chiller loading optimization problems using an improved teaching‐learning‐based optimization algorithm |
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