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

2018; Elsevier BV; Volume: 155; Linguagem: Inglês

10.1016/j.matcom.2018.04.013

1872-7166

Zhi-xin Zheng, Junqing Li, Peiyong Duan,

Advanced Multi-Objective Optimization Algorithms

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.