Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-26T23:03:19.388Z Has data issue: false hasContentIssue false
  • English
  • Français

An evolutionary form design method based on aesthetic dimension selection and NSGA-II

Published online by Cambridge University Press:  04 November 2022

Lingyu Wang ,
Siyu Zhu Open the ORCID record for Siyu Zhu [Opens in a new window] ,
Jin Qi Open the ORCID record for Jin Qi [Opens in a new window]  and
Jie Hu
Lingyu Wang
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Siyu Zhu
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Jin Qi*
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Jie Hu*
Affiliation:
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
*
Authors for correspondence: Jin Qi, E-mail: jinhuaqj@sjtu.edu.cn; Jie Hu, E-mail: hujie@sjtu.edu.cn
Authors for correspondence: Jin Qi, E-mail: jinhuaqj@sjtu.edu.cn; Jie Hu, E-mail: hujie@sjtu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

In the era of rapid product update and intense competition, aesthetic design has been increasingly important in various fields, as aesthetic feelings of customers largely influence their purchase preferences. However, the quantification of aesthetic feeling is still a very subjective process due to vague evaluations. The determination of form parameters according to aesthetics is difficult hitherto. Aesthetic measure recently arises as a prominent tool for this purpose using formulas derived from aesthetic theory. But as revealed by existing studies, it needs to be customized with deterministic and objective methods to be reliable in practice use. To facilitate this application, this paper proposes an evolutionary form design method, integrating aesthetic dimension selection and parameter optimization. After summarizing initial aesthetic dimensions, aesthetic dimension selection based on expert decision-making and particle swarm optimization (PSO) is carried out. With filtered aesthetic dimensions, design parameters are optimized with NSGA-II (non-dominated sorting genetic algorithm). The quality of pareto solutions obtained to be design schemes is assessed by three criteria to conduct sensitivity analysis of cross and mutation probability and population size. Our experiment using bicycle form design shows that the proposed evolutionary form design method can generate numerous and variant aesthetic design schemes rapidly. This is very useful for both product redesign and innovative new product development.

Keywords

Aesthetic measuredimension selectionform designoptimization
Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e33
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Almomani, O (2020) A feature selection model for network intrusion detection system based on PSO, GWO, FFA and GA algorithms. Symmetry (Basel) 12, 1046.CrossRefGoogle Scholar
Aydin, R, Kwong, CK and Ji, P (2014) Simultaneous consideration of remanufactured and new products in optimal product line design. IEEE International Conference on Industrial Engineering and Engineering Management, Dec 09–12, 2014, Malaysia, pp. 1–5.CrossRefGoogle Scholar
Aydin, R, Kwong, CK and Ji, P (2016) Coordination of the closed-loop supply chain for product line design with consideration of remanufactured products. Journal of Cleaner Production 114, 286–298.CrossRefGoogle Scholar
Bashour, M (2006) An objective system for measuring facial attractiveness. Plastic and Reconstructive Surgery 118, 757–774.CrossRefGoogle ScholarPubMed
Birkhoff, GD (1933) Aesthetic Measure. Cambridge: Massachusetts Harvard University Press.CrossRefGoogle Scholar
Burns, K (2006) Atoms of EVE: a Bayesian basis for esthetic analysis of style in sketching. Artificial Intelligence for Engineering Design Analysis and Manufacturing 20, 185–199.CrossRefGoogle Scholar
Cao, Y, Mihardjo, LWW and Parikhani, T (2020) Thermal performance, parametric analysis, and multi-objective optimization of a direct-expansion solar-assisted heat pump water heater using NSGA-II and decision makings. Applied Thermal Engineering 181, 115892.CrossRefGoogle Scholar
Catalano, CE, Giannini, F, Monti, M and Ucelli, G (2007) A framework for the automatic annotation of car aesthetics. Artificial Intelligence for Engineering Design Analysis and Manufacturing 21, 73–90.CrossRefGoogle Scholar
Cio, YSLK, Ma, YC, Vadean, A, Beltrame, G and Achiche, S (2021) Evolutionary layout design synthesis of an autonomous greenhouse using product-related dependencies. Artificial Intelligence for Engineering Design Analysis and Manufacturing 35, 49–64.Google Scholar
Davis, RC (1936) An evaluation and test of Birkhoff's aesthetic measure and formula. Journal of General Psychology 15, 231–240.CrossRefGoogle Scholar
Deb, K, Pratap, A, Agarwal, S and Meyarivan, T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197.CrossRefGoogle Scholar
De Bartolo, D, De Luca, M, Antonucci, G, Schuster, S, Morone, G, Paolucci, S and Iosa, M (2021) The golden ratio as an ecological affordance leading to aesthetic attractiveness. Psych Journal, 1–12.Google ScholarPubMed
Deng, L and Wang, GH (2020) Quantitative evaluation of visual aesthetics of human-machine interaction interface layout. Computational Intelligence and Neuroscience, 2020, 9815937.CrossRefGoogle ScholarPubMed
Eckert, C, Kelly, I and Stacey, M (1999) Interactive generative systems for conceptual design: an empirical perspective. Artificial Intelligence for Engineering Design Analysis and Manufacturing 13, 303–320.CrossRefGoogle Scholar
Fuge, M, Yumer, ME, Orbay, G and Kara, LB (2012) Conceptual design and modification of freeform surfaces using dual shape representations in augmented reality environments. Computer-Aided Design 44, 1020–1032.CrossRefGoogle Scholar
Galitz, WO (1994) It's Time to Clean Your Windows Designing GUIS That Work. New York: John Wiley & Sons, Inc.Google Scholar
Grigoryan, AM and Agaian, SS (2020) Evidence of golden and aesthetic proportions in colors of paintings of the prominent artists. IEEE Multimedia 27, 8–16.CrossRefGoogle Scholar
Hartmann, J, Sutcliffe, A and De Angeli, A (2008) Towards a theory of user judgment of aesthetics and user interface quality. ACM Transactions on Computer–Human Interaction 15, 15.CrossRefGoogle Scholar
Hsiao, SW, Chiu, FY and Lu, SH (2010) Product form design model based on genetic algorithms. International Journal of Industrial Ergonomics 40, 237–246.CrossRefGoogle Scholar
Joloudari, JH, Saadatfar, H, Dehzangi, A and Shamshirband, S (2019) Computer-aided decision-making for predicting liver disease using PSO-based optimized SVM with feature selection. Informatics in Medicine Unlocked 17, 100255.CrossRefGoogle Scholar
Kelly, JC, Wakefield, GH and Papalambros, PY (2011 a) Evidence for using interactive genetic algorithms in shape preference assessment. International Journal of Product Development 13, 168–184.CrossRefGoogle Scholar
Kelly, JC, Maheut, P, Petiot, JF and Papalambros, P (2011 b) Incorporating user shape preference in engineering design optimization. Journal of Engineering Design 22, 627–650.CrossRefGoogle Scholar
Kennedy, J and Eberhart, R (1995) Particle swarm optimization. Proceedings of ICNN'95 – International Conference on Neural Networks, Nov 27–Dec 1, 1995, Vol. 4. Perth, WA, Australia, pp. 1942–1948.CrossRefGoogle Scholar
Knight, T and Sass, L (2010) Looks count: computing and constructing visually expressive mass customized housing. Artificial Intelligence for Engineering Design Analysis and Manufacturing 24, 425–445.CrossRefGoogle Scholar
Koo, LY, Adhitya, A, Srinivasan, R and Karimi, IA (2008) Decision support for integrated refinery supply chains. Part 2. Design and operation. Computers and Chemical Engineering 32, 2787–2800.CrossRefGoogle Scholar
Kwong, CK, Jiang, HM and Luo, XG (2016) AI-based methodology of integrating affective design, engineering, and marketing for defining design specifications of new products. Engineering Applications of Artificial Intelligence 47, 49–60.CrossRefGoogle Scholar
Li, JD, Cheng, KW, Wang, SH, Morstatter, F, Trevino, RP, Tang, JL and Liu, H (2018) Feature selection: a data perspective. ACM Computing Surveys 50, 94.CrossRefGoogle Scholar
Liu, YL (2003) Engineering aesthetics and aesthetic ergonomics: theoretical foundations and a dual-process research methodology. Ergonomics 46, 1273–1292.CrossRefGoogle Scholar
Lo, CH (2018) Application of aesthetic principles to the study of consumer preference models for vase forms. Applied Science (Basel) 8, 1199.CrossRefGoogle Scholar
Lo, CH, Ko, YC and Hsiao, SW (2015) A study that applies aesthetic theory and genetic algorithms to product form optimization. Advanced Engineering Informatics 29, 662–679.CrossRefGoogle Scholar
Longo, F, Padovano, A, Cimmino, B and Pinto, P (2021) Towards a mass customization in the fashion industry: an evolutionary decision aid model for apparel product platform design and optimization. Computers and Industrial Engineering 162, 107742.CrossRefGoogle Scholar
Mars, A, Grabska, E, Slusarczyk, G and Strug, B (2020) Design characteristics and aesthetics in evolutionary design of architectural forms directed by fuzzy evaluation. Artificial Intelligence for Engineering Design Analysis and Manufacturing 34, 147–159.CrossRefGoogle Scholar
Mayer, S and Landwehr, JR (2018) Quantifying visual aesthetics based on processing fluency theory: four algorithmic measures for antecedents of aesthetic preferences. Psychology of Aesthetics Creativity and the Arts 12, 399–431.CrossRefGoogle Scholar
Moshagen, M and Thielsch, MT (2010) Facets of visual aesthetics. International Journal of Human–Computer Studies 68, 689–709.CrossRefGoogle Scholar
Ngo, DCL and Byrne, JG (1998) Aesthetic measures for screen design. Australasian Computer Human Interaction Conference, Nov 30–Dec 04, 1998, Adelaide, Australia, pp. 64–71.CrossRefGoogle Scholar
Ngo, DCL, Lian, ST and Byrne, JG (2003) Modelling interface aesthetics. Information Sciences 152, 25–46.CrossRefGoogle Scholar
Orbay, G and Kara, LB (2012) Shape design from exemplar sketches using graph-based sketch analysis. Journal of Mechanical Design 134, 111002.CrossRefGoogle Scholar
Orbay, G, Yumer, ME and Kara, LB (2012) Sketch-based shape exploration using multiscale free-form surface editing. Artificial Intelligence for Engineering Design Analysis and Manufacturing 26, 337–350.CrossRefGoogle Scholar
Orbay, G, Fu, LT and Kara, LB (2014) Shape spirit: deciphering form characteristics and emotional associations through geometric abstraction. ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Aug 04–07, 2013, Portland, OR, p. V005T06A008.Google Scholar
Orsborn, S and Cagan, J (2009) Automatically generating form concepts according to consumer preference: a shape grammar implementation with software agents. ASME International Design Engineering Technical Conferences/Computers and Information in Engineering Conference, Aug 03–06, 2008, New York, USA, pp. 3–13.Google Scholar
Orsborn, S, Cagan, J, Pawlicki, R and Smith, RC (2006) Creating cross-over vehicles: defining and combining vehicle classes using shape grammars. Artificial Intelligence for Engineering Design Analysis and Manufacturing 20, 217–246.CrossRefGoogle Scholar
Orsborn, S, Cagan, J and Boatwright, P (2008) A methodology for creating a statistically derived shape grammar composed of non-obvious shape chunks. Research in Engineering Design 18, 181–196.CrossRefGoogle Scholar
Perlovsky, LI (2010) Intersections of mathematical, cognitive, and aesthetic theories of mind. Psychology of Aesthetics Creativity and the Arts 4, 11–17.CrossRefGoogle Scholar
Reid, TN, Gonzalez, RD and Papalambros, PY (2010) Quantification of perceived environmental friendliness for vehicle silhouette design. Journal of Mechanical Design 132, 101010.CrossRefGoogle Scholar
Reid, TN, Frischknecht, BD and Papalambros, PY (2012) Perceptual attributes in product design: fuel economy and silhouette-based perceived environmental friendliness tradeoffs in automotive vehicle design. Journal of Mechanical Design 134, 041006.CrossRefGoogle Scholar
Ren, Y and Papalambros, PY (2012) A design preference elicitation query as an optimization process. Journal of Mechanical Design 133, 111004.CrossRefGoogle Scholar
Ren, Y, Scott, C and Papalambros, PY (2014) A scalable preference elicitation algorithm using group generalized binary search. ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Aug 04–07, 2013, Portland, OR, p. V03BT03A005.Google Scholar
Rigau, J, Feixas, M and Sbert, M (2008) Informational aesthetics measures. IEEE Computer Graphics and Applications 28, 24–34.CrossRefGoogle ScholarPubMed
Shi, YH and Eberhart, R (1998) A modified particle swarm optimizer. IEEE International Conference on Evolutionary Computation, May 04–09, 1998, Anchorage, AK, pp. 69–73.CrossRefGoogle Scholar
Shukla, AK, Tripathi, D, Reddy, BR and Chandramohan, D (2020) A study on metaheuristics approaches for gene selection in microarray data: algorithms, applications and open challenges. Evolutionary Intelligence 13, 309–329.CrossRefGoogle Scholar
Song, XF, Zhang, Y, Guo, YN, Sun, XY and Wang, YL (2020) Variable-size cooperative coevolutionary particle swarm optimization for feature selection on high-dimensional data. IEEE Transactions on Evolutionary Computation 24, 882–895.CrossRefGoogle Scholar
Streveler, DJ and Wasserman, AI (1984) Quantitative measures of the spatial properties of screen designs. INTERACT ‘84. First IFIP Conference on ‘Human–Computer Interaction’, Sept 04–07, 1984, Vol. 1. London, UK, pp. 125–133.Google Scholar
Wan, H, Ji, WT, Wu, GQ, Jia, XY, Zhan, X, Yuan, MT and Wang, RL (2021) A novel webpage layout aesthetic evaluation model for quantifying webpage layout design. Information Science 567, 589–608.CrossRefGoogle Scholar
Wannarumon, S, Bohez, ELJ and Annanon, K (2008) Aesthetic evolutionary algorithm for fractal-based user-centered jewelry design. Artificial Intelligence for Engineering Design Analysis and Manufacturing 22, 19–39.CrossRefGoogle Scholar
Wu, XY (2020) Product form evolutionary design system construction based on neural network model and multi-objective optimization. Journal of Intelligent and Fuzzy Systems 39, 7977–7991.CrossRefGoogle Scholar
Wu, CM and Li, P (2019) The visual aesthetics measurement on interface design education. Journal of the Society for Information Display 27, 138–146.CrossRefGoogle Scholar
Wu, HT, Huang, YR, Chen, L, Zhu, YJ and Li, HZ (2022) Shape optimization of egg-shaped sewer pipes based on the nondominated sorting genetic algorithm (NSGA-II). Environmental Research 204, 111999.CrossRefGoogle Scholar
Yang, C, Zhou, YL, Yu, SQ and Yu, CY (2019) User preference enabled intelligent 3D product evolutionary design. Journal of Industrial and Production Engineering 36, 475–492.CrossRefGoogle Scholar
Zeng, D, He, ME, Tang, X and Wang, FG (2020) Cognitive association in interactive evolutionary design process for product styling and application to SUV design. Electronics 9, 1960.CrossRefGoogle Scholar
Zhou, AM, Ouyang, JY, Su, JN and Zhang, ST (2020) Multimodal optimisation design of product forms based on aesthetic evaluation. International Journal of Arts and Technology 12, 128–154.CrossRefGoogle Scholar