Article: A multi-objective optimisation model for the spatial layout of public art Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.61 pp.1 - 23 Abstract: This study proposes a multi-objective optimisation model to address the strategic placement of public art, aiming to balance spatial efficiency, social equity, and economic cost. The data-driven framework incorporates dynamic mobility patterns, multidimensional socioeconomic indices, and urban walkability networks. It concurrently optimises three objectives: maximising weighted accessibility coverage, minimising the Gini coefficient of accessibility, and reducing total expenditure. A novel cognitive heuristic adaptive search algorithm is introduced, which embeds domain knowledge of urban spatial structure to solve this high-dimensional problem. Empirical validation using Manhattan data confirms the algorithm's superior performance, demonstrating a 10.8% to 24.1% improvement in the hypervolume metric over standard multi-objective evolutionary algorithms. The resulting Pareto-optimal solutions quantify clear trade-offs, such as a 142% gain in coverage efficiency or a 48% reduction in accessibility inequality, thereby establishing a scientific basis for equitable cultural resource planning. Inderscience Publishers - linking academia, business and industry through research

Title: A multi-objective optimisation model for the spatial layout of public art

Authors: Xin Wen

Addresses: College Art and Design, Lingnan Normal University, Zhanjiang, 524048, China

Abstract: This study proposes a multi-objective optimisation model to address the strategic placement of public art, aiming to balance spatial efficiency, social equity, and economic cost. The data-driven framework incorporates dynamic mobility patterns, multidimensional socioeconomic indices, and urban walkability networks. It concurrently optimises three objectives: maximising weighted accessibility coverage, minimising the Gini coefficient of accessibility, and reducing total expenditure. A novel cognitive heuristic adaptive search algorithm is introduced, which embeds domain knowledge of urban spatial structure to solve this high-dimensional problem. Empirical validation using Manhattan data confirms the algorithm's superior performance, demonstrating a 10.8% to 24.1% improvement in the hypervolume metric over standard multi-objective evolutionary algorithms. The resulting Pareto-optimal solutions quantify clear trade-offs, such as a 142% gain in coverage efficiency or a 48% reduction in accessibility inequality, thereby establishing a scientific basis for equitable cultural resource planning.

Keywords: public art placement; spatial optimisation; social equity; multi-objective optimisation; data-driven decision support.

DOI: 10.1504/IJICT.2026.153943

International Journal of Information and Communication Technology, 2026 Vol.27 No.61, pp.1 - 23

Received: 20 Jan 2026
Accepted: 20 Feb 2026
Published online: 08 Jun 2026 *

Title: A multi-objective optimisation model for the spatial layout of public art

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