A New Hybrid Cuckoo Quantum-Behavior Particle Swarm Optimization Algorithm and its Application in Muskingum Model

被引：0
作者：
Xiongfa Mai
Han-Bin Liu
Li-Bin Liu
机构：
[1] Nanning Normal University,School of Mathematics and Statistics
来源：
Neural Processing Letters | 2023年 / 55卷
关键词：
Hybrid algorithm; Cuckoo search algorithm; Quantum-behavior particle swarm optimization; Parameter estimation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Based on the Cuckoo Search Algorithm (CSA) and the Quantum-Behavior Particle Swarm Optimization (QPSO), this paper propose a hybrid cuckoo quantum-behavior particle swarm optimization (C-QPSO). At first, the QPSO algorithm is modified by the weighted mean best position and the rapid decreasing contraction-expansion coefficient. After that, elite cooperative mechanism, selection mechanism and the mechanism for preventing premature puberty are designed in C-QPSO. To test the performance of the proposed hybrid algorithm, 12 benchmark functions with different dimensions are solved. It is shown from experiments that the algorithm has strong global optimization ability. Furthermore, our presented C-QPSO algorithm is applied to estimate the parameters of a nonlinear Muskingum model. Finally, some numerical results are given to illustrate the effectiveness of C-QPSO algorithm.
引用
收藏
页码：8309 / 8337
页数：28
相关论文
共 85 条
[1]  
Ouyang A(2021)An improved adaptive genetic algorithm based on DV-Hop for locating nodes in wireless sensor networks Neurocomputing 458 500-510
[2]  
Lu Y(2006)A novel numerical optimization algorithm inspired from weed colonization J Ecol Inform 1 355-366
[3]  
Liu Y(2018)A novel nature-inspired algorithm for optimization: squirrel search algorithm Swarm Evol Comput 44 148-175
[4]  
Mehrabian AR(2021)Applications of new hybrid algorithm based on advanced Cuckoo Search and Adaptive Gaussian Quantum behaved Particle Swarm Optimization in solving ordinary differential equations Expert Syst Appl 172 3117-3141
[5]  
Lucas C(2021)Vector of locally and adaptively aggregated descriptors for image feature representation Pattern Recognit 116 4467-4480
[6]  
Jain M(2022)Semisupervised image classification by mutual learning of multiple self-supervised models Int J Intell Syst 37 1790-1794
[7]  
Singh V(2020)Multimodal transformer with multi-view visual representation for image captioning IEEE Trans Circuits Syst Video 30 2143-2160
[8]  
Rani A(2013)Improved nonlinear Muskingum model with variable exponent parameter J Hydrol Eng 18 482-489
[9]  
Kumar N(2016)Parameters estimation for the new four-parameter nonlinear Muskingum model using the particle swarm optimization Water Resour 30 07016009-1799
[10]  
Shaikh AA(2015)Application of a hybrid optimization method in Muskingum parameter estimation J Irrig Drain Eng 2015 1785-82
123456789