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Enterprise innovation evaluation method based on swarm optimization algorithm and artificial neural network

  • S.I.: Evolutionary Computation based Methods and Applications for Data Processing
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Abstract

Promoting the development of enterprises is a common task of all countries in the world, and the government has always taken promoting enterprise development as a long-term strategy. With the development for market, as an integral part of the social market economy, enterprises have made great contributions to the rapid of national economy. The enterprises are increasing, but the market competition is becoming increasingly fierce, and the weaknesses of enterprises such as limited scale, lax management and difficult financing are constantly highlighted. The appearance of these problems makes enterprises encounter many bottleneck problems in the process of development. Innovation, as the inexhaustible power of enterprise development, plays a significant role in enterprise growth, it is the key to solving the bottleneck problem of enterprise development. If an enterprise wants to achieve considerable development, it must innovate and enhance its competitiveness through innovation. In this context, how to evaluate enterprise innovation has become an important work. This work proposes an IPSO-ATT-MSCNN network via PSO from swarm optimization algorithm and artificial neural network to evaluate enterprise innovation. First, this work designs a multi-scale convolutional neural network (ATT-MSNN) via attention mechanism. This method uses multi-scale convolution to extract features of different scales, which improves the richness of features. This adds attention mechanisms to enhance useful features and reduce the impact of unwanted features such as noise. Second, in view of the performance degradation caused by the random initialization, this paper uses improved PSO algorithm to optimize the initial parameters. Third, this work proposes a series of strategies for promoting enterprise innovation. Finally, this work carried out a comprehensive and systematic experiment for the designed method, and the experiment verified the superiority of this method.

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Data availability

The datasets used during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

This research was supported by Guangxi First-class Discipline Applied Economics Construction Project Fund (grant No.2022GSXKB04) and Guangxi Big Data Analysis of Taxation Research Center of Engineering Construction Projects Fund (grant NO. 2022GSXKB04) and Ministry of Education of China (grant NO. 2021120037), and the Item of Improvement of Basic Scientific Research Ability of Young and Middle-aged College Teachers (No. 2019KY0644).

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Authors and Affiliations

  1. School of Business Administration, Guangxi University of Finance and Economics, Nanning, 530003, China

    Qiansha Zhang, Xiaoxia Zeng, Wei Lo & Binbin Fan

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  1. Qiansha Zhang
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  2. Xiaoxia Zeng
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Correspondence to Xiaoxia Zeng or Wei Lo.

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Zhang, Q., Zeng, X., Lo, W. et al. Enterprise innovation evaluation method based on swarm optimization algorithm and artificial neural network. Neural Comput & Applic 35, 25143–25156 (2023). https://doi.org/10.1007/s00521-023-08317-2

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