A cooperative stochastic configuration network based on differential evolutionary sparrow search algorithm for prediction

• Published in: Systems science & control engineering Vol. 12; no. 1
• Main Authors: Fang, Wenhao, Shen, Bo, Pan, Anqi, Zou, Lei, Song, Baoye
• Format: Journal Article
• Language: English
• Published: Macclesfield Taylor & Francis 31.12.2024; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Accuracy; Algorithms; Configuration management; cooperative; differential evolutionary; Evolutionary algorithms; Heuristic methods; Nodes; Parameters; Performance evaluation; Prediction models; Search algorithms; Simulation; sparrow search algorithm; sparse; Sparse matrices; Stochastic configuration network
• ISSN: 2164-2583; 2164-2583
• DOI: 10.1080/21642583.2024.2314481

Stochastic configuration network (SCN) is a powerful prediction model whose performance is significantly influenced by the configuration of the network parameters. To improve the prediction accuracy of the network, a cooperative stochastic configuration network (CSCN) based on a novel differential evolutionary sparrow search algorithm (DESSA), termed as DESSA-CSCN, is proposed. In the CSCN, the number of hidden layer nodes is adaptively adjusted according to the number of iterations, and the parameters of hidden nodes are cooperatively optimized by using a population-based metaheuristic algorithm. A sparse matrix is introduced to mitigate parameter overfitting caused by the increased number of hidden layer nodes. During parameter optimization, the fitness function is constructed by using the supervision mechanism of the SCN, and the DESSA is utilized as the metaheuristic algorithm to update the weights and biases. In order to verify the effectiveness of the DESSA-CSCN, several simulation experiments have been conducted. The performance of the DESSA is evaluated by the CEC2017 test suit, and the simulation results show that the DESSA exhibits better convergence accuracy and can jump out of local optima more effectively than other algorithms. The performance of the DESSA-CSCN is evaluated by 4 datasets from KEEL, and the simulation results indicate that the DESSA-CSCN achieves better prediction accuracy and faster prediction speed than other models.