Combining Users’ Cognition Noise with Interactive Genetic Algorithms and Trapezoidal Fuzzy Numbers for Product Color Design

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.

Interactive Feature Extraction using Implicit Knowledge Elicitation : Application to Power System Expertise

“…In HBGA [31], in addition to evaluating the fitness value, the user also performs all operations from initialization, mutation, crossover, to selection. IGAs are heavily exploited today in artistic or industrial creation [32,33], where there is a need to draw out the perception and subjective evaluation of the user regarding potential solutions. As the considered outputs are often impossible to compare with classical fitness functions, the user gives here a judgment-based fitness score.…”

Application of artificial bee colony algorithm and back propagation neural network in color evaluation of human–machine interaction interfaces

“…Ding and Dong 25 established a mapping model based on the semantic difference and BPNN between users' color image perceptions and the elements of the product's color design. Yang and Tian 26 presented an advanced interactive genetic algorithm (IGA) by combining users' cognition noise, which included cognition, intermediate, and fatigue phases, to provide users with abundant product color solutions. Zhao et al 27 proposed a multi‐objective bee colony optimization algorithm and applied it to the clothing color design model to help designers provide a variety of design schemes for the model and obtain evaluation results.…”

Kansei Engineering Analysis of Purple-clay Teapot Based on Online Comment Data

“…Tmall platform is a comprehensive shopping website [12] , and its sales categories are complete, most of which are in the form of flagship stores. Therefore, we selected the review data of purple-clay teapot in Tmall e-commerce platform as the research object, and grabbed the sales data of June 7, 2019 through Python program, on which 79,377 related products of purple-clay teapot were sold on Tmall platform.…”