Optimal chiller loading by improved artificial fish swarm algorithm for energy saving

Zhi-xin Zheng, Jun-qing Li and Pei-yong Duan

Mathematics and Computers in Simulation (MATCOM), 2019, vol. 155, issue C, 227-243

Abstract: This study presents an improved artificial fish swarm algorithm (VAFSA) to solve the optimal chiller loading (OCL) problem, using minimal power consumption of chillers and cooling towers as the objective function. In the proposed algorithm, several components are developed, such as initialization method based decimal system, food concentration function, bulletin board approach, target position search mechanism, and position move method. Then, the adjustment strategy of search range of artificial fish, which combines the global search with local search, is proposed for improving the search ability of VAFSA. To testify the performance of VAFSA, three well-known case studies are tested with the comparison with other recently reported approaches. The experimental results show that VAFSA can obtain power saving compared with other approaches, and also with the competitive convergence ability. The proposed algorithm can be used as an attractive alternative method to operate air-conditioning systems.

Keywords: Optimal chiller loading; Energy saving; Artificial fish swarm algorithm (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:155:y:2019:i:c:p:227-243

DOI: 10.1016/j.matcom.2018.04.013

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