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

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.

[1]  Linden J. Ball,et al.  Creative analogy use in a heterogeneous design team: The pervasive role of background domain knowledge , 2016 .

[2]  M. Jha,et al.  Cognitive Dissonance: A Study of Post Purchase Behavior of Consumers in the Context of Financial Products , 2016 .

[3]  Bernard Kusena.  "And Me Too, Do Not Leave Me Out, Please!" Dynamics and Challenges of Food Aid Targeting and Distribution in Mutare District, Zimbabwe, 2000-2010 , 2016 .

[4]  Tao Lin,et al.  A computer-aided coloring method for virtual agents based on personality impression, color harmony, and designer preference , 2018, International Journal of Industrial Ergonomics.

[5]  M. babolhavaeji.  THE ROLE OF PRODUCT COLOR IN CONSUMER BEHAVIOR , 2022 .

[6]  Hyeon‐Jeong Suk,et al.  The Emotional Characteristics of White for Applications of Product Color Design , 2014 .

[7]  Aran Hansuebsai,et al.  A comparative study of the emotional assessment of automotive exterior colors in Asia , 2011 .

[8]  Ashok K. Goel,et al.  On the Role of Analogy in Resolving Cognitive Dissonance in Collaborative Interdisciplinary Design , 2014, ICCBR.

[9]  Lois B. Wexner.  The degree to which colors (hues) are associated with mood-tones. , 1954 .

[10]  Xiaoyan Sun,et al.  Interactive genetic algorithms with individual's fuzzy fitness , 2011, Comput. Hum. Behav..

[11]  Lauren I. Labrecque,et al.  Exciting red and competent blue: the importance of color in marketing , 2012 .

[12]  Mitsuo Nagamachi,et al.  Kansei Engineering: A new ergonomic consumer-oriented technology for product development , 1995 .

[13]  Runliang Dou,et al.  Multi-stage interactive genetic algorithm for collaborative product customization , 2016, Knowl. Based Syst..

[14]  I. C. Parmee,et al.  Reducing user fatigue within an interactive evolutionary design system using clustering and case-based reasoning , 2009 .

[15]  Jyh-Rong Chou,et al.  Automatic design support and image evaluation of two-coloured products using colour association and colour harmony scales and genetic algorithm , 2007, Comput. Aided Des..

[16]  Jyh-Rong Chou,et al.  A Kansei evaluation approach based on the technique of computing with words , 2016, Adv. Eng. Informatics.

[17]  Runliang Dou,et al.  An interactive genetic algorithm with the interval arithmetic based on hesitation and its application to achieve customer collaborative product configuration design , 2016, Appl. Soft Comput..

[18]  Waldemar Karwowski.  Kansei Engineering and Kansei Evaluation , 2006 .

[19]  Hui Shao,et al.  Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex Problems , 2018, Complex..

[20]  Scott Klasky,et al.  Personalized Search Inspired Fast Interactive Estimation of Distribution Algorithm and Its Application , 2017, IEEE Transactions on Evolutionary Computation.

[21]  Mitsuo Nagamachi,et al.  Kansei engineering as a powerful consumer-oriented technology for product development. , 2002, Applied ergonomics.

[22]  Shih-Wen Hsiao,et al.  An image evaluation approach for parameter-based product form and color design , 2006, Comput. Aided Des..

[23]  József Mezei,et al.  How different are ranking methods for fuzzy numbers? A numerical study , 2013, Int. J. Approx. Reason..

[24]  Chiao Fei Hsu,et al.  A consultation and simulation system for product color planning based on interactive genetic algorithms , 2013 .

[25]  P. Machado,et al.  Experiments in Computational Aesthetics An Iterative Approach to Stylistic Change in Evolutionary Art , 2008 .

[26]  Emilie Poirson,et al.  Study of the convergence of Interactive Genetic Algorithm in iterative user's tests: application to car dashboard design , 2010 .

[27]  Zhenyu Gu,et al.  Data driven webpage color design , 2016, Comput. Aided Des..

[28]  Yang Chen,et al.  DPM-IEDA: Dual Probabilistic Model Assisted Interactive Estimation of Distribution Algorithm for Personalized Search , 2019, IEEE Access.

[29]  Dong Wei,et al.  Product color design based on multi-emotion , 2013 .

[30]  Sung-Bae Cho,et al.  Application of interactive genetic algorithm to fashion design , 2000 .

[31]  Min-Yuan Ma,et al.  A design decision-making support model for customized product color combination , 2007, Comput. Ind..

[32]  Annie W. Y. Ng,et al.  Color associations among designers and non-designers for common warning and operation concepts. , 2018, Applied ergonomics.

[33]  Ming-Huang Lin,et al.  Examining the "Later Wow" through Operating a Metaphorical Product , 2014 .

[34]  Meghna Babbar-Sebens,et al.  Interactive Genetic Algorithm with Mixed Initiative Interaction for multi-criteria ground water monitoring design , 2012, Appl. Soft Comput..

[35]  Man Ding,et al.  Multi-Working Mode Product Color Planning Using Evolutionary Algorithm and Swarm Intelligence , 2013 .

[36]  Hung-Cheng Tsai,et al.  Computer Aided Product Color Design with Artificial Intelligence , 2007 .

[37]  Ching-Hsue Cheng,et al.  Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation , 2002, Eur. J. Oper. Res..

[38]  Huang Yong,et al.  Interactive Evolutionary Computation with Fitness Noise and Its Convergence Robustness , 2007 .

[39]  Han Huang,et al.  Domination landscape in evolutionary algorithms and its applications , 2019, Soft Comput..

[40]  Seung-Hwan Choi,et al.  Interactive Genetic Algorithm for Designing the Appearance of Software Robot using Homologous Chromosome Representation , 2008 .

[41]  F. Heider.  Attitudes and cognitive organization. , 1946, The Journal of psychology.

[42]  Ferenc Szeifert,et al.  Interactive evolutionary computation in process engineering , 2005, Comput. Chem. Eng..

[43]  Shih-Wen Hsiao,et al.  Transforming the natural colors of an image into product design: A computer-aided color planning system based on fuzzy pattern recognition , 2015 .