基于交互式差分进化算法的产品优化设计实验.
Saved in:
| Title: | 基于交互式差分进化算法的产品优化设计实验. (Chinese) |
|---|---|
| Alternate Title: | Product optimization design experiment based on interactive differential evolution algorithm. (English) |
| Authors: | 李海港, 郭广颂, 王胜, 张勇 |
| Source: | Experimental Technology & Management; Nov2025, Vol. 42 Issue 11, p67-71, 5p |
| Subject Terms: | DIFFERENTIAL evolution, MULTI-objective optimization, EVOLUTIONARY algorithms, ERGONOMICS, SURROGATE-based optimization, OPERATIONS research, NEW product development, CONSUMER preferences |
| Abstract (English): | [Objective] To efficiently obtain optimal product design solutions that better align with user preferences by leveraging their distribution characteristics across design variables, this study proposes a novel variable preference surrogate model (VPSM) and an adaptive genetic strategy within an interactive differential evolution (IDE) framework. The core challenge in preference-driven optimization lies in accurately capturing subjective user intentions while minimizing the user's evaluation effort, especially when design variables influence multiple objectives in complex ways. By explicitly differentiating how variables affect different objectives and integrating preference learning directly into the evolutionary mechanism, this study aims to bridge the gap between computational optimization and human-centered design. [Methods] The proposed approach begins by statistically analyzing the decision variables and classifying them into independent attributes, those affecting only a single objective, and correlated attributes, those influencing multiple objectives simultaneously. Gaussian functions are then used to model user preferences for each attribute type. Based on the feedback from user-evaluated solutions, preference degrees for independent and correlated attributes are inferred for unevaluated individuals, forming a VPSM to estimate fitness values and reduce computational or user evaluation costs. An adaptive genetic strategy dynamically adjusts crossover and mutation probabilities according to VPSM estimates and population state: crossover is decreased for high-fitness individuals to preserve quality solutions, whereas mutation is increased in low-diversity regions to promote exploration. The surrogate model is iteratively updated with new user feedback, continuously refining the preference model and guiding the evolutionary search toward solutions that better align with user intentions. As new user feedback is incorporated, the surrogate model is iteratively updated, continuously improving the model's accuracy and guiding the evolutionary search toward solutions that better reflect user intentions over time. [Results] To evaluate the performance of the proposed method, comparative experiments were conducted against two ablation algorithms, one without VPSM and another without the adaptive genetic strategy, as well as four state-of-the-art evolutionary optimization algorithms. These evaluations were performed on three widely used benchmark test functions and a real-world automotive side-profile design problem to ensure generalizability and practical relevance. The experimental results show that the proposed method consistently outperforms competing algorithms across multiple metrics, including convergence speed, solution quality, and stability. It reduces required user evaluations by up to 40% in some test cases, significantly alleviating user fatigue. Moreover, the proposed method demonstrates greater robustness and faster convergence in later search stages, confirming its effectiveness in refining solutions as additional preference information becomes available. [Conclusions] This study demonstrates that integrating the VPSM and adaptive genetic strategy within the IDE framework provides an efficient and effective solution for preference-driven product design optimization. By explicitly modeling user preferences across variable types and adapting the search accordingly, the proposed method reduces user fatigue while guiding successful optimization toward high-quality, user-aligned solutions. Validation on both synthetic benchmarks and a real-world engineering design scenario underscores its reliability. This study highlights the importance of combining variable-sensitive preference modeling with adaptive evolutionary operators to handle the complexity of human preferences in multi-objective settings. Future work will explore deep learning-based surrogate models and multi-user preference fusion to enhance the flexibility and scalability of the approach. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): | 考虑个体偏好在不同变量上的分布特征, 采用交互式差分进化方法求解产品优化设计问题可以获得 更好的优化解。基于此, 该文提出了变量偏好代理模型及相应的自适应遗传策略。首先, 用高斯函数描述属 性的偏好性;其次, 根据用户已评价的个体, 计算未评价个体的独立属性与关联属性的偏好度, 构建变量偏 好代理模型, 估计未评价个体适应值;再次, 用自适应遗传策略生成新种群;最后, 基于推荐个体和用户评 价信息更新代理模型。以汽车侧身造型设计问题为例, 对比2 种消融算法和4 种相关进化优化算法, 该文所 提方法可以高效获得最优设计方案. [ABSTRACT FROM AUTHOR] |
| Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
Nájsť tento článok vo Web of Science Be the first to leave a comment!