重庆邮电大学学报（自然科学版）2025，Vol.37Issue(1)：76-84,9.DOI:10.3979/j.issn.1673-825X.202312250432
改进粒子群优化算法和双分支网络的耦合效率预测
Coupling efficiency prediction of improved particle swarm optimization algorithm and double branch network
摘要
Abstract
To address the issues of long simulation time and high resource consumption in software-based analysis of cou-pling efficiency in hybrid waveguide structures,this paper proposes a neural network model to predict coupling efficiency,thereby reducing time and resource usage.To solve the problem of significant step size differences between independent var-iables in multivariable problems,which limits the feature extraction capability of a single network,a dual-branch network is introduced to extract features for different variables.An improved particle swarm optimization(PSO)algorithm is applied to automatically optimize the hyperparameters of the dual-branch network,further improving model prediction accuracy.Exper-iments on the hybrid waveguide structure coupling efficiency dataset show that the proposed improved PSO-dual-branch net-work reduces the mean squared error by 88.17 percentage points compared to the support vector machine(SVM)algorithm,21.17 percentage points compared to the recurrent neural network(RNN),and 16.81 percentage points compared to the standard PSO algorithm,validating the effectiveness and superiority of the proposed model and optimization algorithm.关键词
粒子群优化算法/双分支神经网络/混合波导结构耦合效率Key words
particle swarm optimization/double branch neural network/hybrid waveguide coupling efficiency分类
信息技术与安全科学引用本文复制引用
赖春红,陈先勤,姜小明,陈帅,王悦,刘思创,邹林熹..改进粒子群优化算法和双分支网络的耦合效率预测[J].重庆邮电大学学报（自然科学版）,2025,37(1):76-84,9.基金项目
国家自然科学基金项目(62105050)National Natural Science Foundation of China(62105050) （62105050）
