为了提高人体尺寸预测的效率和准确性，该文提出了GBWO-ENN(Grey Black Wolf Optimization-Elman Neural Network)的方法。针对传统灰狼算法易于陷入局部最优和无法平衡全局与局部搜索的平衡性问题，提出了GBWO算法。该算法融合黑寡妇优化算法中蜘蛛的运动方式对灰狼优化算法中α狼位置更新进行了优化，通过非线性递减的方法降低了收敛系数，并且提出了按位置等级更新种群的策略。随后采用GBWO算法对Elman神经网络的权值和阈值进行优化，并将GBWO-ENN模型应用于三维人体尺寸预测。实验结果表明，GBWO-ENN模型结构简单，能够准确预测人体尺寸，具有较好的预测能力。
A Body Size Prediction Model Incorporating GBWO and ENN
In order to improve the efficiency and accuracy of human body size prediction,we propose the GBWO-ENN(Grey Black Wolf Optimization-Elman Neural Network)method.Aiming at the problems that the traditional grey wolf algorithm is easy to fall into local optimum and unable to balance the equilibrium of global and local searches,the GBWO algorithm is proposed,which optimizes the α-wolf position update in the grey wolf optimization algorithm by incorporating the movement of spiders in the Black Widow Optimization algorithm.It reduces the convergence coefficient by a non-linear diminishing method,and proposes a strategy of updating the population according to positional rank.Subsequently,the GBWO algorithm is used to optimize the weights and thresholds of the Elman neural network,and the GBWO-ENN model is applied to the 3D human body size prediction aspect.The experimental results show that the GBWO-ENN model has a simple structure,can accurately predict human body size,and has good predictive ability.
万方数据
维普
