Interface layout design of agricultural harvester based on analytic network process and quantum particle swarm optimization

This study proposes an intelligent design method based on ANP-QPSO (Analytic Network Process-Quantum Particle Swarm Optimization) for the complex layout and multi-objective optimization of the operation interface of agricultural harvesters. The ANP method is utilized to classify the functional hierarchy of interface elements and analyze their weights, thereby quantifying user cognitive demands and aesthetic criteria. Combined with the QPSO algorithm, a multi-objective optimization model is established to reduce cognitive load and enhance operational efficiency, enabling efficient search and collaborative adjustment of interface layouts. Experimental results demonstrate a high consistency between element weights and user visual attention in the optimized layout. The strong correlation between eye-tracking data and ANP weights validates the rationality of the design. Innovatively integrating subjective and objective evaluation metrics, this research establishes a data-driven interface design framework, offering new insights for optimizing human-machine interaction in agricultural machinery. Future work will focus on expanding the algorithm’s applicability to diverse scenarios and exploring intelligent design models integrating multimodal data fusion, thereby advancing agricultural informatization and intelligent development.