An improved particle swarm optimization algorithm and its application in energy saving optimization of central air conditioning

Abstract

In order to solve the problem that the traditional particle swarm algorithm is difficult to converge to the optimal solution in the late iteration, and enhance the global search ability of traditional particle swarm algorithm, the inertial weights of the random perturbation sine adjustment particles were added at the initial and end of the search. At the same time, some Benchmark functions are used to test the improved particle swarm algorithm, and the results show that the improved particle swarm algorithm has obtained remarkable progress on convergence speed and precision. In this paper, the improved particle swarm optimization algorithm is utilized to optimize the whole system of central air conditioning and the optimal working point corresponding to minimum system energy consumption can be confirmed.