一种量子磷虾群融合算法及其应用  

A Quantum Krill Herd Fusion Algorithm and Its Application

作　　者：冯增喜[1,2] 赵锦彤 李诗妍 杨亚龙 陈海越[1] 张聪 Feng Zengxi;Zhao Jintong;Li Shiyan;Yang Yalong;Chen Haiyue;Zhang Cong(School of Building Services Science and Engineering,Xi'an University of Architecture and Technology,Xi'an 710055,China;Anhui Key Laboratory of Intelligent Building and Building Energy Conservation,Anhui Jianzhu University,Hefei 230022,China)

机构地区：[1]西安建筑科技大学建筑设备科学与工程学院,陕西西安710055 [2]安徽建筑大学智能建筑与建筑节能安徽省重点实验室,安徽合肥230022

出　　处：《系统仿真学报》2022年第10期2142-2151,共10页Journal of System Simulation

基　　金：国家重点研发计划(2017YFC0704104-03);陕西省科技厅专项(2017JM6106);安徽建筑大学智能建筑与建筑节能安徽省重点实验室2018年度开放课题(IBES2018KF08)。

摘　　要：针对磷虾群算法和量子进化算法的缺陷,提出了一种量子磷虾群融合算法(quantum krill herd fusion algorithm,QKH)。该算法通过采用双链实数编码量子磷虾位置,加快收敛速度,避免量子观测的随机性和复杂性;通过利用动态调整的量子磷虾群旋转门更新磷虾位置,提升收敛精度,提高量子旋转相位的确定效率;通过改进的量子全干扰交叉策略,避免算法陷入局部最优,提升优化效率。通过经典测试函数验证了所提算法的优势。建立了QKH-BPNN空调负荷预测模型,仿真结果表明:该模型具有更好的准确性和稳定性。Aiming at the defects of krill herd algorithm and quantum evolutionary algorithm, a quantum krill herd fusion algorithm(QKH) is proposed. The algorithm uses double-chain real numbers to encode the krill position, which can speed up the convergence speed, and avoids the randomness and complexity of quantum observations. The dynamically adjusted quantum krill herd rotation phase update strategy improves the convergence accuracy, and the efficiency of determining the quantum rotation phase. The introduction of an improved quantum full interference crossover strategy can prevent the fusion algorithm from falling into a local optimum, and can improve the optimization efficienal. The advantages of the quantum krill herd fusion optimization algorithm are verified by the classic test functions. A QKH-BPNN prediction model is established for air conditioning load forecasting, and the results show that the model has better accuracy and higher stability.

关 键 词：量子磷虾群融合算法 双链实数编码 量子磷虾群旋转相位 改进的量子全干扰交叉 QKH-BPNN预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]