School of Construction Machinery, Chang'an University, Xi'an 710064, China.
Comput Intell Neurosci. 2019 Aug 1;2019:1019749. doi: 10.1155/2019/1019749. eCollection 2019.
Product color plays a vital role in shaping brand style and affecting users' purchase decision. However, users' preferences about product color design schemes may vary due to their cognition differences. Although considering users' perception of product color has been widely performed by industrial designers, it is not effective to support this activity. In order to provide users with plentiful product color solutions as well as embody users' preference into product design process, involving users in interactive genetic algorithms (IGAs) is an effectual way to find optimum solutions. Nevertheless, cognition difference and uncertainty among users may lead to various understanding in line with IGA progressing. To address this issue, this study presents an advanced IGA by combining users' cognition noise which includes cognition phase, intermediate phase, and fatigue phase. Trapezoidal fuzzy numbers are employed to represent uncertainty of users' evaluations. An algorithm is designed to find key parameters through similarity calculation between RGB value and their area proportion of two individuals and users' judgment. The interactive product color design process is put forward with an instance by comparing with an ordinary IGA. Results show that (1) knowledge background will significantly affect users' cognition about product colors and (2) the proposed method is helpful to improve convergence speed and evolution efficiency with convergence increasing from 67.5% to 82.5% and overall average evolutionary generations decreasing from 18.15 to 15.825. It is promising that the proposed method can help reduce users' cognition noise, promote convergence, and improve evolution efficiency of interactive product color design.
产品颜色在塑造品牌风格和影响用户购买决策方面起着至关重要的作用。然而，由于用户认知的差异，他们对产品颜色设计方案的偏好可能会有所不同。尽管工业设计师已经广泛考虑了用户对产品颜色的感知，但这并不能有效地支持这一活动。为了向用户提供丰富的产品颜色解决方案，并将用户的偏好融入产品设计过程中，让用户参与交互式遗传算法（IGA）是找到最佳解决方案的有效方法。然而，用户之间的认知差异和不确定性可能会导致在 IGA 进展过程中产生各种理解。针对这个问题，本研究提出了一种通过结合用户认知噪声的先进 IGA，其中包括认知阶段、中间阶段和疲劳阶段。梯形模糊数用于表示用户评估的不确定性。设计了一种算法，通过比较两个人的 RGB 值与其面积比例的相似性计算以及用户的判断，来找到关键参数。通过与普通 IGA 进行比较，提出了交互式产品颜色设计过程的实例。结果表明：（1）知识背景会显著影响用户对产品颜色的认知；（2）该方法有助于提高收敛速度和进化效率，收敛度从 67.5%提高到 82.5%，整体平均进化世代从 18.15 代减少到 15.825 代。该方法有望帮助减少用户的认知噪声，促进收敛，并提高交互式产品颜色设计的进化效率。
