Article: An optimisation model for the graphic design process based on generative AI Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.95 pp.25 - 48 Abstract: As the pace of digital transformation accelerates, the demand for graphic design is growing exponentially. Traditional design workflows are highly labour-intensive, inefficient, and ill-suited to meet diverse creative needs. To tackle these issues, this paper introduces a generative artificial intelligence-based model for optimising graphic design workflows. Built upon generative adversarial networks, the model incorporates a self-attention mechanism to enhance semantic relationships between design elements. Leveraging latent space search, this paper presents an iterative framework that optimises design workflows, enabling designers to adjust layouts with constraints on partial attributes. Experimental findings demonstrate that the proposed model lowers the overlap rate to 0.17 and attains an alignment score of 0.64, substantially surpassing the baseline method. Ablation studies confirm the efficacy of the model's key components. By effectively boosting design efficiency and layout rationality, this research provides a new technical pathway for the optimisation of graphic design workflows. Inderscience Publishers - linking academia, business and industry through research

Title: An optimisation model for the graphic design process based on generative AI

Authors: Xiaodan Li

Addresses: School of Media Art and Design, Wuxi Vocational Institute of Arts & Technology, Wuxi, 214200, China

Abstract: As the pace of digital transformation accelerates, the demand for graphic design is growing exponentially. Traditional design workflows are highly labour-intensive, inefficient, and ill-suited to meet diverse creative needs. To tackle these issues, this paper introduces a generative artificial intelligence-based model for optimising graphic design workflows. Built upon generative adversarial networks, the model incorporates a self-attention mechanism to enhance semantic relationships between design elements. Leveraging latent space search, this paper presents an iterative framework that optimises design workflows, enabling designers to adjust layouts with constraints on partial attributes. Experimental findings demonstrate that the proposed model lowers the overlap rate to 0.17 and attains an alignment score of 0.64, substantially surpassing the baseline method. Ablation studies confirm the efficacy of the model's key components. By effectively boosting design efficiency and layout rationality, this research provides a new technical pathway for the optimisation of graphic design workflows.

Keywords: graphic design; process optimisation; generative adversarial network; attention mechanism; latent space search.

DOI: 10.1504/IJICT.2026.156130

International Journal of Information and Communication Technology, 2026 Vol.27 No.95, pp.25 - 48

Received: 03 Apr 2026
Accepted: 24 May 2026
Published online: 04 Sep 2026 *

Title: An optimisation model for the graphic design process based on generative AI

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