Multiemotional product color design using gray theory and nondominated sorting genetic algorithm‐III

Abstract

When users select products, they consider the emotional experience resulting from the color of the product. However, the emotional demands of users for product color are multidimensional and diverse. It is very important yet difficult to accurately grasp multiemotional image requirements and effectively convert them into design elements. Therefore, multiemotional product color design (MEPCD) has become a very important and challenging research topic. In this article, a novel MEPCD system using gray theory (GT) and nondominated sorting genetic algorithm‐III (NSGA‐III) is proposed to effectively solve the MEPCD problem. First, the image perception spaces of users, which exist in different emotional dimensions, were collected using factor analysis and the semantic differential technique. Then, GT was used to establish a multidimensional emotional product color image evaluation model. Finally, NSGA‐III was used to optimize and design a multiemotional color scheme for a product. Furthermore, according to actual conditions, an MEPCD system was established based on the proposed method. The design case study shows that the method and design system proposed in this article have a certain range of applicability and can effectively improve the practicality of MEPCD.

Correlation Analysis between the Emotion and Aesthetics for Chinese Classical Garden Design Based on Deep Transfer Learning

“…Firstly, the fuzzy processing technology was used to extract the color combination scheme from the source image, and then the interactive genetic algorithm was used to complete the fitness evaluation of the color scheme population, so as to obtain the product design scheme that meets the user's perceptual needs. Ding et al [ 12 ] established a multidimensional psychological product design model using grey theory and other methods to solve the problem of multidimensional psychological demand in product design. They used a nondominated sorting multiobjective optimization algorithm to optimize the design and finally built an intelligent design system based on this method.…”

Product color emotional design based on a convolutional neural network and search neural network

“…The optimal product color scheme was obtained through optimization of the evaluation of the population, and a product color recommendation system was developed. Ding 47,48 uses a non‐dominated sorting GA‐III to design and optimize a multi‐dimensional emotional product color scheme. These studies utilized intelligent algorithms to generate accurate solutions rapidly, which greatly improves the design efficiency and design success rate.…”

An XGBoost based evaluation methodology of product color emotion design

“…Scholars usually use literature studies, user interviews, market investigation, and other methods to collect vocabulary at the first stage. They then employ lexical cluster analysis and factor analysis (FA) to screen out target vocabularies or pairs (Ding and Dong, 2020). At the second stage, SD is often used to carry out user perceptual experiments on product samples.…”