A Method for Predicting Merchandise Price Based on Deep Neural Network by Grey Wolf-Particle Swarm Algorithm Optimization

Highly precise and rapid global-optimal merchandise price prediction can offer reference value for enterprises' strategic decisions, such as procurement and risk assessment in the economic market. This paper proposes a merchandise price prediction method based on improving Deep Neural Networks(DNN) using the Grey Wolf-Particle Swarm Algorithm. To address the gradient vanishing or explosion caused by the input of multi-feature initial parameters into deep neural networks in merchandise price prediction, this paper optimizes the weight and bias parameters of DNN by improving the Chaotic Theory of the Grey Wolf-Particle Swarm Optimization Algorithm (IC-GWO-PSO). The concept of circular mapping in the GWO algorithm is introduced to avoid the problem of the particle swarm being prone to local optima and achieve a jump search to obtain the global optimal solution of the parameters of DNN. Moreover, the tent mapping in chaos theory is adopted to control the adjustment speed of particles and prevent them from exceeding the solution boundary, enhancing the exploration efficiency and convergence accuracy of the algorithm in the solution space. Experimental results indicate that IC-GWO-PSO outperforms baseline methods such as GWO-PSO and IGWO-IPSO in procurement price prediction. The three indicators, RMSE, R, and Fitness, reach 2.49, 0.9937, and 1583, respectively, demonstrating its performance advantages in global rapid solution seeking and precise price prediction.