The core issue in handling constrained multi-objective optimization problems (CMOP) is how to maintain a balance between objectives and constraints. However, existing constrained multi-objective evolutionary algorithms (CMOEAs) often fail to achieve the desired performance when confronted with complex feasible regions. Building upon this theoretical foundation, a two-stage archive-based constrained multi-objective evolutionary algorithm (CMOEA-TA) based on genetic algorithms (GA) is proposed. In CMOEA-TA, First stage: The archive appropriately relaxes constraints based on the proportion of feasible solutions and constraint violations, compelling the population to explore more search space. Second stage: Sharing valuable information between the archive and the population, while embedding constraint dominance principles to enhance the feasibility of solutions. In addition an angle-based selection strategy was used to select more valuable solutions to increase the diversity of the population. To verify its effectiveness, CMOEA-TA was tested on 54 CMOPs in 4 benchmark suites and 7 state-of-the-art algorithms were compared. The experimental results show that it is far superior to seven competitors in inverse generation distance (IGD) and hypervolume (HV) metrics.

Purpose: Virtual simulation experiments, which are widely utilized by educational institutions, can mitigate the high cost, high risk, long cycle and complex implementation issues associated with traditional experimental teaching, and have become an effective supplement to conventional teaching. However, users often exhibit insufficient learning stickiness when utilizing virtual simulation experiments. This paper aims to improve users’ learning stickiness through the Dynamic Design of User Interface.Design/methodology/approach: This paper summarizes dynamic design strategies for virtual simulation experiments based on the theory of emotional design. A comparative experiment was designed with the dynamic design strategies of user interface as the independent variable. Two hundred university students were invited to participate and divided into two groups, and they completed the comparative experiment and the same questionnaire.Findings: The results indicate that the experimental group that utilized dynamic design exhibited significantly higher learning stickiness than the control group that did not utilize dynamic design (P < 0.05). Moreover, feedback on dynamic design varies between genders, with men experiencing a more pronounced increase in learning stickiness compared to women. In conclusion, the dynamic design strategies of the user interface, based on the theory of emotional design, are effective and significantly enhance users’ learning stickiness.Originality/value: This paper uses the related theories of design to provide a solution to the problems existing in the teaching environment: enhancing learning stickiness can be improved through the dynamic design of the user interface. Additionally, tailored dynamic design strategies can also be formulated based on the gender distribution in different fields of study.

Digital technology is widely applied in sports for environmental simulation, motion detection, data analysis, and diagnostic feedback, yet Sports Virtual Training Systems (SVTS) often suffer from low user technology acceptance due to cumbersome interaction workflows and high operational complexity. Drawing upon gamification theory and the emotional design framework, this study proposes a gamification-driven interaction optimization approach across visceral, behavioral, and reflective levels. A comparative experiment was conducted with 360 participants stratified into professional, semi-professional, and amateur athletic proficiency groups. Questionnaires assessed perceived ease of use, perceived usefulness, and behavioral intention. Results indicate that the experimental group scored significantly higher than the control group across all three dimensions, confirming the approach's efficacy in enhancing technology acceptance. Subgroup analysis revealed heterogeneous effects: amateur users exhibited significant improvements in all dimensions, semi-professional athletes demonstrated significant gains in perceived usefulness and behavioral intention, while professional athletes showed no significant change. These findings underscore the effectiveness of the proposed optimization pathway and emphasize that athletic proficiency moderates its impact, with greater benefits observed among lower-skill users. The approach offers actionable design implications for SVTS interaction optimization and suggests that design strategies should be tailored to the target population's overall skill level.

Rapid global urbanization has increased the demand for residential construction, rendering traditional design methods insufficient. This study proposes an innovative approach integrating parametric techniques with fine-tuning diffusion models to automate the modeling and rendering of residential area planning. Specifically, this method first utilizes Grasshopper programming to develop a parametric design system that can automatically create 3D models of residential areas. Subsequently, a new dataset was constructed to fine-tune the diffusion model, enabling it to generate high-quality renderings that conform to specific architectural styles, types, and viewpoints. The proposed method transforms the traditional scheme refinement, modeling, and rendering processes into a generative design process. In a subjective evaluation of 2880 generated images by ten designers, the proposed method obtained higher scores than Midjourney, DALL E3, and Stable Diffusion in usability (81%), building style consistency (87%), and realism (92%). Results indicate that this method can efficiently generate diverse design options at the preliminary design stage. This study provides a practical workflow for the rapid generation of residential area models and renderings.