Abstract
In the global employment landscape, career planning education is crucial for enhancing the competitiveness of higher education students. Within this framework, engagement in collegiate competitions has become a widely adopted method for developing professional competencies, cultivating a passion for learning, and advancing career aspirations. However, the traditional approach to competition selection predominantly relies on personal experience and unsystematic guidance, which often leads to undesirable results, including imbalanced skill development, wasted energy, squandered time, and diminished job-market competitiveness. Since the traditional approach does not take into account skill coverage, time commitment, and energy expenditure when addressing the multi-objective and multi-constraint nature of competition selection. To solve this problem, this study proposes an innovative energy-aware staged-elite multi-objective particle swarm optimisation algorithm (ES-MOPSO) to develop an optimal competition selection system for higher education. In addition, the proposed algorithm features a staged iteration and elite-update strategy to efficiently optimise multiple objectives while avoiding local optima. The developed system integrates energy-aware reward modelling and structured data management to enable optimal, personalised competition selection for students. In the context of student competition selection, experimental validation on representative scenarios demonstrates that the ES-MOPSO outperforms established optimisers, such as MOPSO, SS-MOPSO, and NSGA-II, according to the hypervolume (HV) metric. Moreover, the case study analysis indicates that the proposed system (1) reduces the coefficient of variation (CV) in skill development by over 30%, effectively resolving imbalances in skill growth; (2) decreases fluctuations in energy investment by approximately 10%, facilitating more scientific and efficient time management; and (3) achieves higher competition scores with equivalent energy input or requires less energy to attain comparable scores. Furthermore, scalability experiments demonstrate the system’s robust performance in complex environments.
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Acknowledgements
This study was supported in part by the National Natural Science Foundation of China under Grant 62177029, the National Science Fund for Distinguished Young Scholars of China under Grant 82205180, and in part by the Startup Foundation for Introducing Talent of Nanjing University of Posts and Telecommunications under Grant NY225012 and Jiangsu Specially-Appointed Professor Foundation of China under Grant RK002STP25013. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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This study was performed in line with the principles of the Declaration of Helsinki. Regarding the timeline of ethical approval, preliminary informed consent materials were distributed to participants on 01/03/2025, prior to the formal ethical approval granted by the Academic Ethics and Ethics Committee of Nanjing University of Posts and Telecommunications on 15/04/2025 (No. 2025B011). While this timeline categorizes the approval as retrospective relative to the initial participant contact, this specific procedural sequence was strictly mandated by local institutional guidelines and national regulations. According to Article 11 of the ’Measures for the Ethical Review of Biomedical Research Involving Human Subjects’ (issued by the National Health Commission of the PRC, revised in 2016), investigators are legally required to prepare and submit the informed consent materials as a mandatory prerequisite for the formal ethical review application. The preliminary consent process was conducted solely for regulatory compliance, and no experimental data were collected during this phase. Furthermore, during the formal data collection phase in May 2025, informed consent was strictly executed and re-confirmed in accordance with the granted ethical approval. As detailed in the Informed consent section, participants were required to carefully read the study instructions and click an “agree and continue" button before formally completing the questionnaire. This digital consent interface explicitly informed participants of the research purpose, data usage, privacy protection principles, and their right to voluntarily participate or withdraw at any time. This process fully guaranteed the participants’ right to information and autonomy. All collected data were completely anonymous, ensuring the ethical risk associated with this study remained extremely low.
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Ruan, T., Lu, W., Song, Y. et al. ES-MOPSO: an energy-aware staged-elite MOPSO system for optimal competition selection in higher education. Humanit Soc Sci Commun (2026). https://doi.org/10.1057/s41599-026-07308-7
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DOI: https://doi.org/10.1057/s41599-026-07308-7
