Large-scale particle swarm optimization (PSO) has long been a hot topic due to the following reasons: Swarm diversity preservation is still challenging for current PSO variants for large-scale optimization problems, resulting in difficulties for PSO in balancing its exploration and exploitation. Furthermore, current PSO variants for large-scale optimization problems often introduce additional operators to improve their ability in diversity preservation, leading to increased algorithm complexity. To address these issues, this paper proposes a dual-competition-based particle update strategy (DCS), which selects the particles to be updated and corresponding exemplars with two rounds of random pairing competitions, which can straightforwardly benefit swarm diversity preservation. Furthermore, DCS confirms the primary and secondary exemplars based on the fitness sorting operation for exploitation and exploration, respectively, leading to a dual-competition-based swarm optimizer. Thanks to the proposed DCS, on the one hand, the proposed algorithm is able to protect more than half of the particles from being updated to benefit diversity preservation at the swarm level. On the other hand, DCS provides an efficient exploration and exploitation exemplar selection mechanism, which is beneficial for balancing exploration and exploitation at the particle update level. Additionally, this paper analyzes the stability conditions and computational complexity of the proposed algorithm. In the experimental section, based on seven state-of-the-art algorithms and a recently proposed large-scale benchmark suite, this paper verifies the competitiveness of the proposed algorithm in large-scale optimization problems.

Z-Generation (Gen Z) students have access to more information than any other generation at their age. Mentor-student relationship is the core interpersonal relationship during the graduate stage. How do Gen Z graduate students perceive the role of their mentor, when "anything they want to know is only a click away"? Based on Self-Determination Theory and Compensatory Internet Use Theory, this study investigated the relationship between mentor-student relationship and smartphone dependence among 1,432 Chinese social science and humanities (SSH) graduate students. A moderated mediation model was constructed to focus on the role of research self-efficacy and growth mindset. The results showed that a positive mentor-student relationship is found to have a significant negative predictive effect on smartphone dependence. Scientific self-efficacy is identified as a mediator in the relationship between the mentor-student relationship and smartphone dependence. Additionally, growth mindset is found to moderate the predictive effect of the mentor-student relationship on research self-efficacy. The results of this study revealed the mechanism of mentor-student relationship on graduate students' smartphone dependence, offering valuable insights for rethinking the role of mentors in the modern educational environment.