Abstract
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.
Highlights
As an essential component of a vision system, color can trigger complex aesthetic sensations and psychological reactions and impact on the cognition and emotions of people [1]
Users’ fatigue and cognitive dissonance are ubiquitous and will gradually arise with the evolution process of interactive genetic algorithm (IGA), which can be defined as fitness noise and will influence the performance of interactive evolutionary computation (IEC) [31]. e former can be caused by a lot of repetitive work, tedious operation and visual weariness, and the latter may be attributed to the user’s knowledge and experience discrepancy. ey have constituted the issues and obstacles to the application of IGAs
If the number of satisfactory individuals meets the preset requirements. If it is the last evolutionary generation there are 30% participants majored in the industrial design and 35% participants from other majors who have not found the required 6 satisfactory solutions. e convergence rate is increased from 67.5% to 82.5%, which indicates that the proposed method can improve the convergence of the interactive product color design
Summary
Combining Users’ Cognition Noise with Interactive Genetic Algorithms and Trapezoidal Fuzzy Numbers for Product Color Design. Users’ preferences about product color design schemes may vary due to their cognition differences. 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. 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
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
