Research on the Optimization Design of Human-Machine Interaction in Single-Pilot Aircraft Cockpits

Yichen Zhou

doi:10.54254/2753-8818/2025.GL22567

Research Article

Open access

Published on 15 May 2025

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Zhou,Y. (2025). Research on the Optimization Design of Human-Machine Interaction in Single-Pilot Aircraft Cockpits. Theoretical and Natural Science,109,1-7.

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Research on the Optimization Design of Human-Machine Interaction in Single-Pilot Aircraft Cockpits

Yichen Zhou *^,1,

¹ School of Engineering, Shanghai Ocean University, Shanghai, China, 201306

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/2025.GL22567

Abstract

With the rise of the Single-Pilot Operation (SPO) mode, the human-machine interaction design of aircraft cockpits faces new challenges. This paper focuses on optimizing human-machine interaction in single-pilot aircraft cockpits by constructing a three-dimensional evaluation system based on the NSGA-II multi-objective optimization strategy. This system comprehensively analyzes the reachability of operating components (), the contrast of color information (), and the cognitive load of pilots (). Data are derived from assumptions and simulation analyses based on literature and theory, covering factors such as pilot characteristics, component layout, color contrast limits, and flight mission complexity. By generating Pareto front solutions, the paper reveals trade-offs among the three objectives, providing a new quantitative decision-making basis and research direction for cockpit design.

Keywords

SPO, Aircraft Cockpit, Human-Machine Interaction, NSGA-II Multi-Objective Optimization Model, Pareto Front

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References

[1]. Zhang, T., Shi, T., & Wei, Z. (2021). Adaptive adjustment method for automation level in single-crew operation. Chinese Journal of Safety Science, (08), 155–164.

[2]. Xu, W. (2022). Seven perspectives on user-centered design: From automation to intelligent aircraft cockpits. Applied Psychology, (04), 291–313.

[3]. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

[4]. Ye, K., Luo, T., Yang, W., Bai, Y., & Song, M. (2022). Optimization design of aircraft cockpit layout under multiple constraints. Human Ergonomics, (05), 84–87.

[5]. Ye, K., & Wei, S. (2017). Aircraft cockpit layout optimization design based on genetic algorithm. Journal of Armament and Equipment Engineering, (03), 108–110.

[6]. Yang, Y. (2023). A study on the factors affecting operating errors of cockpit toggle switches [Master’s thesis, Civil Aviation University of China].

[7]. Li, X. (2024). Analysis and optimization of general aircraft cockpit human-machine ergonomics based on motion characteristics [Master’s thesis, Civil Aviation Flight University of China].

[8]. Li, J. (2020). Digital interface visualization design and its cognitive load study in aircraft cockpits [Master’s thesis, Nanjing University of Aeronautics and Astronautics].

[9]. Shen, Z., Chen, G., Tu, W., et al. (2024). Human-computer interaction interface design of flight simulator based on situation awareness. Scientific Reports, 14, 27842.

[10]. Kang, N., Wang, S., Wang, Y., Wang, X., & Wang, J. (2024). Interface layout optimization algorithm based on intelligent optimization and multi-criteria decision model. Intelligent Computers and Applications, (09), 26–33.

[11]. Styler, B. K., Deng, W., Simmons, R., Admoni, H., Cooper, R., & Ding, D. (2024, March). Exploring control authority preferences in robotic arm assistance for power wheelchair users. In Actuators (Vol. 13, No. 3, p. 104). MDPI.

Cite this article

Zhou,Y. (2025). Research on the Optimization Design of Human-Machine Interaction in Single-Pilot Aircraft Cockpits. Theoretical and Natural Science,109,1-7.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of CONF-MPCS 2025 Symposium: Leveraging EVs and Machine Learning for Sustainable Energy Demand Management

Conference website: https://www.confmpcs.org/glasgow.html

ISBN：978-1-80590-103-7(Print) / 978-1-80590-104-4(Online)

Conference date: 16 May 2025

Editor：Anil Fernando, Mustafa Istanbullu

Series: Theoretical and Natural Science

Volume number: Vol.109

ISSN：2753-8818(Print) / 2753-8826(Online)

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