Solving chiller loading optimization problems using an improved teaching‐learning‐based optimization algorithm |
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| Authors: | Pei‐yong Duan Jun‐qing Li Yong Wang Hong‐yan Sang Bao‐xian Jia |
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| Affiliation: | 1. School of Information, Shandong Normal University, Jinan, PR China;2. College of Computer Science, Liaocheng University, Liaocheng, PR China;3. State Key Laboratory of Synthetic Automation for Process Industries, Northeastern University, Shenyang, PR China |
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| Abstract: | 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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| Keywords: | energy conversation optimal chiller loading teaching‐learning‐based optimization |
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