实验技术与管理2025，Vol.42Issue(11)：67-71,5.DOI:10.16791/j.cnki.sjg.2025.11.007
基于交互式差分进化算法的产品优化设计实验
Product optimization design experiment based on interactive differential evolution algorithm
摘要
Abstract
[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.关键词
差分进化/交互/变量/代理模型/偏好Key words
differential evolution/interactive/variable/surrogate model/preference分类
信息技术与安全科学引用本文复制引用
李海港,郭广颂,王胜,张勇..基于交互式差分进化算法的产品优化设计实验[J].实验技术与管理,2025,42(11):67-71,5.基金项目
国家自然科学基金(62273348) （62273348）
河南省科技攻关项目(242102211095) （242102211095）
江苏省学位与研究生教育教学改革项目(JGKT25_B038) （JGKT25_B038）
中国矿业大学教学研究项目(2025YJSJG054,2025JY14,2025KC21) （2025YJSJG054,2025JY14,2025KC21）
