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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Vydáno v:Optimal control applications & methods Ročník 39; číslo 1; s. 65 - 77
Hlavní autoři: Duan, Pei‐yong, Li, Jun‐qing, Wang, Yong, Sang, Hong‐yan, Jia, Bao‐xian
Médium: Journal Article
Jazyk:angličtina
Vydáno: Glasgow Wiley Subscription Services, Inc 01.01.2018
Témata:
Algorithms
Decoding
energy conversation
Exploitation
Exploration
Learning
Machine learning
optimal chiller loading
Optimization
teaching‐learning‐based optimization
ISSN:0143-2087, 1099-1514
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0143-2087
1099-1514
DOI:10.1002/oca.2334