Affiliations:
School of Building Services Science and Engineering, Xi’an University of Architecture and Technology, Xi’an, ShaanXi, China
Correspondence:
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Corresponding author. Junqi Yu, No. 13, Yanta Road, Beilin District, Xi’an, Shaanxi Province, Xi’an University of Architecture and Technology Xi’an, China. E-mail: [email protected].
Abstract: An improved mayfly algorithm is proposed for the energy saving optimization of parallel chilled water pumps in central air conditioning system, with the minimum energy consumption of parallel pump units as the optimization objective and the speed ratio of each pump as the optimization variable for the solution. For the problem of uneven random initialization of mayflies, the variable definition method of Circle chaotic mapping is used to make the initial position of the population uniformly distributed in the solution space, and the mayfly fitness value and the optimal fitness value are incorporated into the calculation of the weight coefficient, which better balances the global exploration and local exploitation of the algorithm. For the problem that the algorithm is easy to fall into the local optimum at the later stage, a multi-subpopulation cooperative strategy is proposed to improve the global search ability of the algorithm. Finally, the performance of the improved mayfly algorithm is tested with two parallel pumping system cases, and the stability and time complexity of the algorithm are verified. The experiments show that the algorithm can get a better operation strategy in solving the parallel water pump energy saving optimization problem, and can achieve energy saving effect of 0.72% 8.68% compared with other optimization algorithms, and the convergence speed and stability of the algorithm have been significantly improved, which can be better applied to practical needs.
Keywords: Energy saving optimization, parallel water pump, improved mayfly algorithm, circle chaotic mapping, multi subpopulation cooperative strategy