Title: Research on parallelisation prediction for the size of industrial economies based on FA-SSA optimisation SVM

Authors: Xiaofang Shi

Addresses: Department of Finance and Economics, Taiyuan University, Taiyuan, 030001, China

Abstract: Aiming at the problems of slow prediction speed and low accuracy of industrial economy scale, this paper proposes a prediction model based on support vector machine (SVM) and parallel framework. Firstly, sparrow search algorithm (SSA) algorithm improved by elite inverse strategy and firefly disturbance strategy is adopted to optimise the kernel function parameter γ and penalty factor C of SVM model, and the optimal parameters of SVM model are obtained by using the MapReduce parallel computing framework, which improves the prediction speed and accuracy of industrial economic scale. The results reveal that the mean absolute error, mean square error and explained variance of the proposed method are 0.31, 0.17 and 0.85, respectively. Therefore, the proposed model can be applied to the actual industrial economy scale prediction.

Keywords: size of industrial economies; support vector machine; SVM model; sparrow search algorithm; SSA; parallel framework.

DOI: 10.1504/IJBIC.2026.155398

International Journal of Bio-Inspired Computation, 2026 Vol.28 No.1, pp.24 - 34

Received: 15 Mar 2024
Accepted: 30 Oct 2024
Published online: 31 Jul 2026 *

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