基于多策略改进麻雀搜索算法的并联冷机系统节能优化

针对并联冷机系统负荷分配优化问题,提出一种基于多策略的改进麻雀搜索算法,以系统功耗最小为优化目标,以各冷机的部分负荷率为优化变量进行求解.在改进算法中,首先,针对基本麻雀搜索算法初始解的质量差且不均匀问题,引入混沌序列机制对位置初始化;然后,针对算法初期易早熟导致搜索精度低的问题,提出将粒子群算法中的速度概念引入发现者的位置更新公式中,提高算法的寻优精度.为了避免算法长期陷入局部最优,结合狼群算法猛狼的跟随策略优化跟随者的位置,自适应调整个体权重提高算法的收敛速度;接着,选取两个测试案例对所提出算法的性能进行详细测试,并与其他常用算法对比,改进的麻雀搜索算法在案例中最高分别可节能17.8%和23.97%;最后,运用实际系统仿真平台验证所提出改进算法收敛快、运行时间短、鲁棒性好的优点.

Optimization of parallel chillers system based on multi-strategy improved sparrow search algorithm for energy saving

Aimed at the optimal chiller loading problem in parallel chillers systems, a multi-strategy-based improved sparrow search algorithm is proposed. The aim of the optimization problem is to minimize chillers power consumption, and the partial load ratio of each chiller is used as the optimization variable. In the improved algorithm, firstly, the chaotic sequence mechanism is introduced to improve the quality and diversity of the initial solution. Secondly, in order to enhance the optimization accuracy, the speed concept in the particle swarm algorithm is proposed to update producer position. To avoid the algorithm from falling into a local optimum for a long time, the following strategy of the wolf pack algorithm is combined to update scrounger position and adjust the individual weight adaptively to improve the convergence speed of the algorithm. Finally, two test cases are selected to confirm the performance of the proposed algorithm in detail, and compared with other commonly used algorithms, the improved sparrow search algorithm can save up by 17.8% and 23.97%, respectively. By using the actual system simulation platform, it is verified that the improved algorithm has the advantages of fast convergence, short running time and good robustness.