Abstract
This paper presents and compares evolutionary algorithms, such as ant colony and genetic algorithms, and exact approaches for color recipe prediction. The objective is to optimize the color formulation step in reproducing the desired shades by minimizing the color differences CMC (2:1) between the target color and the color obtained by the proposed recipe. Objective criteria were used to compare the proposed methods. Four direct dyes (CI Direct Red 227, CI Direct Orange 34, CI Direct Blue 85 and CI Direct Black 22) were used in this study for dyeing bleached woven fabrics (100% cotton).
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References
Agahian, F., Amirshahi, S.H.: A New matching strategy: trial of the principal component coordinates. Color. Res. Appl. 33(1), 10–18 (2008)
Almodarresi, E.S.Y., Mokhtari, J., Almodarresi, S.M.T., Nouri, M., Shams-Nateri, A.: A scanner based neural network technique for color matching of dyed cotton with reactive dye. Fibers Polym. 14(7), 1196–1202 (2013)
Ameri, F., Moradian, S., Amani Tehran, M., Faez, K.: The use of fundamental color stimulus to improve the performance of artificial neural network color match prediction systems. Iranian J. Chem. Chem. Eng. Int. English Ed. 24(4), 53–61 (2005)
Abuiziah, I., Shakarneh, N.: A review of genetic algorithm optimization: operations and applications to water pipeline systems, world academy of science, engineering and technology. Int. J. Math. Comput. Sci. 7(12) (2013)
Bishop, J.M., Bushnell, M.J., Westland, S.: Application of neural network to computer recipe prediction. Color. Res. Appl. 16(1), 3–9 (1991)
Blum, C.: Ant colony optimization: Introduction and recent trends. Phys. Life Rev. 2, 353–373 (2005)
Carlos C.A.C.: An introduction to evolutionary algorithms and their applications. In: Ramos, F.F., Larios Rosillo, V., Unger, H. (eds.) ISSADS 2005. LNCS, vol. 3563, pp. 425–442. Springer, Heidelberg (2005). https://doi.org/10.1007/11533962_39
Clarke, F.J.J., McDonald, R., Rigg, B.: Modification to the JPC79 colour-difference formula. JSDC 100, 128–132 (1984)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge, MA (2004)
Hai-tao, L., Ai-song, S., Bing-sen, Z.: A Dyeing Color Matching Method Combining RBF Neural Networks with Genetic Algorithms. IEEE Computer Society, pp.701–707 (2007)
Holland, J.: Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor. (Technical Report ORA Projects 01252 and 08226). University of Michigan, Department of Computer and Communication Sciences, Ann Arbor (1975)
Jawahar, M., Babu, C., Kannan, N., Kondamudi-Manobhai, M.: Artificial neural networks for colour prediction in leather dyeing on the basis of a tristimulus system. Color. Technol. 131(1), 48–57 (2015)
Kubelka, P., Munk, F.: Ein Beitrag zur Optik der Farbanstriche. Z. Tech. Phys. 12, 593–601 (1931)
Kubelka, P.: New contributions to the optics of intensely light-scattering materials. Part I. J. Opt. Soc. Am. 38, 448–457 (1948)
Kubelka, P.: New contributions to the optics of intensely light-scattering materials. Part II non-homogeneous layers. J. Opt. Soc. Am. 44, 330–334 (1954)
Leardi, R.: Genetic algorithms, chemical and biochemical data. Analysis 1, 631–653 (2009)
McGinnis, P.H.: Spectrophotometric color matching with the least squares technique. Col. Engin. 5, 22–27 (1967)
Nobbs, J.H.: Kubelka-Munk theory and the prediction of reflectance. Rev. Prog. Color. Relat. Top. 15, 66–75 (1985)
Olaechea, R., Rayside, D., Guo, J., Czarnecki, K.: Comparison of exact and approximate multi-objective optimization for software product lines. In: Proceedings of the 18th International Software Product Line Conference, vol. 1, pp. 92–101 (2014)
Vikhar, P.A.: Evolutionary algorithms: a critical review and its future prospects. In: International Conference on Global Trends in Signal Processing, Information Computing and Communication (2016)
Shams-Nateri, A.: Prediction of dye concentrations in a three-component dye mixture solution by a PCA-derivative spectrophotometry technique. Color. Res. Appl. 35(1), 29–33 (2010)
Shams-Nateri, A.: Dye concentrations determination in ternary mixture solution by using colorimetric algorithm. Iranian J. Chem. Chem. Eng. Int. English Edn. 30(4), 51–61 (2011)
Wright, W.D.: The Measurement of Color, 4th edn. Hilger, London, U.K. (1969)
Wyszecki, G., Stiles, W.S.: Color Science: Concepts and Methods, Quantitative Data and Formulae, 2nd edn. Wiley, New York (2000)
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Chaouch, S., Moussa, A., Ben Marzoug, I., Ladhari, N. (2022). Application and Comparison Between Exact and Evolutionary Algorithms for Color Recipe Prediction. In: Msahli, S., Debbabi, F. (eds) Advances in Applied Research on Textile and Materials - IX. CIRATM 2020. Springer Proceedings in Materials, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-031-08842-1_13
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