Spot color is widely applied to printing and packing in modern industry, which can satisfy the individualization requirements and express the emotion of products. Color prediction is the core technique for spot color restoration. In this paper, a method that combines the least squares method and gravitation search algorithm is proposed to address the color prediction by using the absorption spectrum. Firstly, the spectral transmittance of the thin film with high transmission and low reflectance characteristics is researched to find the absorbance. Secondly, the least squares method is used to ascertain the primary colors of the spot color. Thirdly, an enhanced quantum gravitation search algorithm is designed to predict the spot color. The predicted results on the 30 spot colors show that the proposed method has higher accuracy in comparison with the three existed methods. The color differences between the prepared spot colors and the reproduced spot colors are all less than 3, in which 75% of the color differences are less 1 and 35% of the color differences are less 0.1. All the results confirm that the proposed method can predict the spot color accurately.
BordesPAndrivonPLiX, et al.Overview of color gamut scalability. IEEE Trans Circ Syst Video Technol2017; 27: 1580–1594.
2.
YangRXuYXieC, et al.Kubelka-Munk double constant theory of digital rotor spun color blended yarn. Dye Pigm2019; 165: 151–156.
3.
DupontD. Study of the reconstruction of reflectance curves based on tristimulus values: comparison of methods of optimization. Color Res Appl2002; 27: 88–99.
4.
LuoRZhouSSJiangN, et al.Mathematical model and implementation of the density-based Gravure spot-color matching. Adv Mater Res2011; 174: 56–59.
5.
SatyaSGBBosantaRB. Estimation of point spread function of an optical microscope using stochastic minimization of least square errors. J Opt2020; 22: 055603–055603.
6.
PesalKMarkkuHBirgittaM, et al.Color mixing and color separation of pigments with concentration prediction. Color Res Appl2008; 33: 461–469.
7.
Li S, Manabe Y and Chihara K. Estimating reflectance parameters of an object from saturated spectral images. In: international conference on computer graphics imaging and visualization, 26–28 July 2006. Sydney, Australia: IEEE, pp.1–5 (accession number 9077076).
8.
KandiSGTehranMA. Color recipe prediction by Genetic Algorithm. Dye Pigm2017; 74: 677–683.
9.
SabrineCAliMImedBM. Colour recipe prediction using ant colony algorithm: principle of resolution and analysis of performances. Color Technol2019; 135: 349–360.
10.
LeeKRRichardsonAJWalowitE, et al.Predicting color matches from luminance matches. J Opt Soc Am A2020; 37: A35–A43.
11.
JianJZhanZZhangJ. Large-scale evolutionary optimization: a survey and experimental comparative study. Int J Mach Learn Cybern2020; 11: 729–745.
12.
WuXWuS. An elitist quantum-inspired evolutionary algorithm for the flexible job-shop scheduling problem. J Intell Manuf2017; 28: 1441–1457.
13.
RashediEPourHNSaryazdiS. GSA: a gravitational search algorithm. Inform Sci2019; 179: 2232–2248.
14.
MosorovV. The Lambert-Beer law in time domain form and its application. Appl Radiat Isotope2017; 128: 1–5.
15.
ChenLShenHHengC, et al.Algorithm for predicting the length of each color fiber in mixed-wool fiber assemblies based on the transmission image. Text Res J2019; 90: 357–366.
16.
YangLBaiJZhangR, et al.The optimization of color-prediction models for colored cotton fiber yarns. Text Res J2019; 89: 4007–4014.
17.
ZhangYZhouSCaoC, et al.An absorbance-based prediction method of spot color formula for PET film. Spectrosc Spectra Anal2019; 39: 415–420.
HuangLQinC. A novel modified gravitational search algorithm for the real world optimization problem. Int J Mach Learn Cybern2019; 10: 2993–3002.
20.
MosaMA. Real-time data text mining based on Gravitational Search Algorithm. Exp Syst Appl2019; 137: 117–129.
21.
JiangJYangXMengX, et al.Enhance chaotic gravitational search algorithm (CGSA) by balance adjustment mechanism and sine randomness function for continuous optimization problems. Physica A2020; 537: 1–17.
22.
LinJTChiuC. A hybrid particle swarm optimization with local search for stochastic resource allocation problem. J Intell Manuf2018; 29: 481–495.
23.
NagraAAHanFLingQ, et al.An improved hybrid method combining gravitational search algorithm with dynamic multi swarm particle swarm optimization. IEEE Access2019; 7: 50388–50399.
24.
ZhuLHeSWangL, et al.Feature selection using an improved gravitational search algorithm. IEEE Access2019; 7: 114440–114448.
25.
EsmatRElahehRHosseinN. A comprehensive survey on gravitational search algorithm. Swarm Evol Computat2018; 41: 141–158.
26.
ZhaoXZhangZXieY, et al.Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization. Energy2020; 195: 1–15.
27.
LiuMZhangFMaY, et al.Evacuation path optimization based on quantum ant colony algorithm. Adv Eng Informatic2016; 30: 259–267.
YanTLiuF. Quantum-behaved particle swarm optimization algorithm based on the two-body problem. Chin J Electron2019; 28: 569–576.
30.
LiLJiaoLZhaoJ, et al.Quantum-behaved discrete multi-objective particle swarm optimization for complex network clustering. Pattern Recognit2017; 63: 1–14.
31.
MohadesehSHosseinNMaliheMF. A quantum inspired gravitational search algorithm for numerical function optimization. Inform Sci2014; 267: 83–100.
32.
PelusiDMascellaRTalliniL, et al.Improving exploration and exploitation via a hyperbolic gravitational search algorithm. Knowl Base Syst2020; 193: 1–13.
33.
GaoZHMaCBLuoYG, et al.IMA health state evaluation using deep feature learning with quantum neural network. Eng Appl Artif Intell2018; 76: 119–129.
34.
GongRZhangXLiX, et al.A method for optimising settings of colour primaries for wide colour gamut displays. Optik2018; 154: 216–225.
35.
ZhangYZhouSLiC. Study on the lightfastness of ink based on visible and infrared spectral analysis. Sci Adv Mater2019; 11: 1773–1780.
36.
GaoZHMaCBSheZY, et al.An enhanced deep extreme learning machine for integrated modular avionics health state estimation. IEEE Access2018; 6: 65813–65823.
37.
HwangJPKimSParkCK. Development of a color matching algorithm for digital transfer textile printing using an artificial neural network and multiple regression. Text Res J2015; 85: 1076–1082.
