{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T02:34:02Z","timestamp":1784169242031,"version":"3.55.0"},"reference-count":38,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,24]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Geometric and floral models are an important part of clothing and have been used for thousands of years. Although the styles of geometric models and flower models have undergone changes over the centuries, they are still one of the important factors of clothing patterns, the important carrier of aesthetics, and the manifestation of people\u2019s spiritual views and cultural needs. The development and application of digital printing technology have freed people from excessive dependence on sewing and embroidery processes. Therefore, while deeply studying the design of clothing patterns, this work sorted and analyzed the geometric flower models of clothing through interactive genetic algorithms, and optimized programming to enrich the models of clothing with geometric textures. The results showed that the deviation value of geometric flower pattern design was constantly decreasing, while the optimal strategy value was constantly increasing. The mean deviation value was 0.82, which was a decrease of 0.21 on the seventh day compared with the first day; the mean value of the optimal strategy value was 0.84, which was an increase of 0.19 on the seventh day compared with the first day. The visual effect and creativity of the clothing flower pattern design under the interactive genetic algorithm are better than the traditional flower pattern design, and the visual effect and creativity under the interactive genetic algorithm are 9% higher than the traditional one.<\/jats:p>","DOI":"10.1515\/jisys-2023-0269","type":"journal-article","created":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T15:36:27Z","timestamp":1719243387000},"source":"Crossref","is-referenced-by-count":7,"title":["Design of geometric flower pattern for clothing based on deep learning and interactive genetic algorithm"],"prefix":"10.1515","volume":"33","author":[{"given":"Xu","family":"Cong","sequence":"first","affiliation":[{"name":"School of Digital Arts and Design, Dalian Neusoft University of Information , Dalian , 116023, Liaoning , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjia","family":"Zhang","sequence":"additional","affiliation":[{"name":"DanDong Bejor Clothing Co., Ltd , Dandong , 118000, Liaoning , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2024,6,24]]},"reference":[{"key":"2025120517251264029_j_jisys-2023-0269_ref_001","doi-asserted-by":"crossref","unstructured":"Mi JC, Ju HP. A study on the fashion design by pattern deconstruction of tailored jacket. J Korean Soc Fash Des. 2018;18(1):57\u201376.","DOI":"10.18652\/2018.18.1.4"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_002","doi-asserted-by":"crossref","unstructured":"Wang W, Wei J, Wang F. Preference analysis of traditional handicraft brocade pattern in fashion art. J Phys: Conf Ser. 2021;1790(1):012028\u201334.","DOI":"10.1088\/1742-6596\/1790\/1\/012028"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_003","doi-asserted-by":"crossref","unstructured":"Dong Z, Jiang G, Huang G, Cong H. A web-based 3D virtual display framework for warp-knitted seamless garment design. Int J Cloth Sci Technol. 2018;30(3):332\u201346.","DOI":"10.1108\/IJCST-05-2017-0060"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_004","unstructured":"Shafiq H, Zhang N. Eco-friendly concept: Female clothing design inspired by plastic bag\u2019s pattern. Costume Guide. 2020;9(3):33\u20137."},{"key":"2025120517251264029_j_jisys-2023-0269_ref_005","doi-asserted-by":"crossref","unstructured":"Greder KC, Pei J, Shin J. Design in 3D: A computational fashion design protocol. Int J Cloth Sci Technol. 2020;32(4):537\u201349.","DOI":"10.1108\/IJCST-07-2019-0110"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_006","doi-asserted-by":"crossref","unstructured":"Lu S, Mok PY, Jin X. A new design concept: 3D to 2D textile pattern design for garments. Comput Des. 2017;89(7):35\u201349.","DOI":"10.1016\/j.cad.2017.03.002"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_007","doi-asserted-by":"crossref","unstructured":"Sun YK. Development of pattern design and fashion cultural products fused with the Byeoljeon Motif. Korean Soc Sci Art. 2020;38(3):37\u201350.","DOI":"10.17548\/ksaf.2020.06.30.37"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_008","doi-asserted-by":"crossref","unstructured":"Hashimoto M, Nakamoto K. Process planning for die and mold machining based on pattern recognition and deep learning. J Adv Mech Des Syst Manuf. 2021;15(2):15\u20139.","DOI":"10.1299\/jamdsm.2021jamdsm0015"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_009","doi-asserted-by":"crossref","unstructured":"Li K, Xing J, Hu W. D2C: Deep cumulatively and comparatively learning for human age estimation. Pattern Recognit. 2017;66(4):95\u2013105.","DOI":"10.1016\/j.patcog.2017.01.007"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_010","doi-asserted-by":"crossref","unstructured":"Yang H, Jing S, Yi Z. Layout hotspot detection with feature tensor generation and deep biased learning. IEEE Trans Comput Des Integr Circuits Syst. 2017;8(6):1175\u201387.","DOI":"10.1109\/TCAD.2018.2837078"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_011","doi-asserted-by":"crossref","unstructured":"Ren LL, Lu JW, Feng JJ, Zhou  J. Multi-modal uniform deep learning for RGB-D person re-identification. Pattern Recognit: J Pattern Recognit Soc. 2017;72(4):446\u201357.","DOI":"10.1016\/j.patcog.2017.06.037"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_012","doi-asserted-by":"crossref","unstructured":"Li T, Zhao H, Zhou X. Method of short-circuit fault diagnosis in transmission line based on deep learning. Int J Pattern Recognit Artif Intell. 2022;36(5):64\u20136.","DOI":"10.1142\/S0218001422520097"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_013","doi-asserted-by":"crossref","unstructured":"Manjon JV, Romero JE, Coupe P. Deep learning based MRI contrast synthesis using full volume prediction. Biomed Phys Eng Express. 2022;8(1):015013\u201321.","DOI":"10.1088\/2057-1976\/ac3c64"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_014","unstructured":"Long X, Gong X, Zhou H. Deep learning based data prefetching in CPU-GPU unified virtual memory. arXiv e-prints. 2022;5(6):41\u20132."},{"key":"2025120517251264029_j_jisys-2023-0269_ref_015","doi-asserted-by":"crossref","unstructured":"Abdullah JM, Ahmed T. Fitness dependent optimizer: Inspired by the bee swarming reproductive process. IEEE Access. 2019;7:43473\u201386.","DOI":"10.1109\/ACCESS.2019.2907012"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_016","doi-asserted-by":"crossref","unstructured":"Cheng R, Jin Y. A competitive swarm optimizer for large scale optimization. IEEE Trans Cybern. 2014;45(2):191\u2013204.","DOI":"10.1109\/TCYB.2014.2322602"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_017","doi-asserted-by":"crossref","unstructured":"Zhao W, Wang L, Zhang Z. Supply-demand-based optimization: A novel economics-inspired algorithm for global optimization. IEEE Access. 2019;7:73182\u2013206.","DOI":"10.1109\/ACCESS.2019.2918753"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_018","doi-asserted-by":"crossref","unstructured":"Shabani A, Asgarian B, Salido M, Gharebaghi SA. Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems. Expert Syst Appl. 2020;161:113698.","DOI":"10.1016\/j.eswa.2020.113698"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_019","doi-asserted-by":"crossref","unstructured":"Das B, Mukherjee V, Das D. Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems. Adv Eng Softw. 2020;146:102804.","DOI":"10.1016\/j.advengsoft.2020.102804"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_020","doi-asserted-by":"crossref","unstructured":"Kaveh A, Khanzadi M, Moghaddam MR. Billiards-inspired optimization algorithm; a new meta-heuristic method. Structures. 2020;1722\u201339.","DOI":"10.1016\/j.istruc.2020.07.058"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_021","doi-asserted-by":"crossref","unstructured":"Houssein Essam H, Saad MR, Hashim FA, Shaban H, Hassaballah M. L\u00e9vy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems. Eng Appl Artif Intell. 2020;94:103731.","DOI":"10.1016\/j.engappai.2020.103731"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_022","doi-asserted-by":"crossref","unstructured":"Li S, Chen H, Wang M, Heidari AA, Mirjalili S. Slime mould algorithm: A new method for stochastic optimization. Future Gener Computer Syst. 2020;111:300\u201323.","DOI":"10.1016\/j.future.2020.03.055"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_023","doi-asserted-by":"crossref","unstructured":"De Vasconcelos Segundo EH, Mariani VC, Dos Santos Coelho L. Design of heat exchangers using falcon optimization algorithm. Appl Therm Eng. 2019;156:119\u201344.","DOI":"10.1016\/j.applthermaleng.2019.04.038"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_024","doi-asserted-by":"crossref","unstructured":"Moosavi SHS, Bardsiri VK. Poor and rich optimization algorithm: A new human-based and multi populations algorithm. Eng Appl Artif Intell. 2019;86:165\u201381.","DOI":"10.1016\/j.engappai.2019.08.025"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_025","doi-asserted-by":"crossref","unstructured":"Sulaiman MH, Mustaffa Z, Saari MM, Daniyal H. Barnacles mating optimizer: A new bio-inspired algorithm for solving engineering optimization problems. Eng Appl Artif Intell. 2020;87:103330.","DOI":"10.1016\/j.engappai.2019.103330"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_026","doi-asserted-by":"crossref","unstructured":"Yapici H, Cetinkaya N. A new meta-heuristic optimizer: Pathfinder algorithm. Appl Soft Comput. 2019;78:545\u201368.","DOI":"10.1016\/j.asoc.2019.03.012"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_027","doi-asserted-by":"crossref","unstructured":"Cao Y, Wang Q, Wang Z, Jermsittiparsert K, Shafiee M. A new optimized configuration for capacity and operation improvement of CCHP system based on developed owl search algorithm. Energy Rep. 2020;6:315\u201324.","DOI":"10.1016\/j.egyr.2020.01.010"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_028","doi-asserted-by":"crossref","unstructured":"Shayanfar H, Gharehchopogh FS. Farmland fertility: A new metaheuristic algorithm for solving continuous optimization problems. Appl Soft Comput. 2018;71:728\u201346.","DOI":"10.1016\/j.asoc.2018.07.033"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_029","doi-asserted-by":"crossref","unstructured":"Zhao W, Zhang Z, Wang L. Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Eng Appl Artif Intell. 2020;87:103300.","DOI":"10.1016\/j.engappai.2019.103300"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_030","doi-asserted-by":"crossref","unstructured":"Kamboj VK, Nandi A, Bhadoria A, Sehgal S. An intensify Harris Hawks optimizer for numerical and engineering optimization problems. Appl Soft Comput. 2020;89:106018.","DOI":"10.1016\/j.asoc.2019.106018"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_031","doi-asserted-by":"crossref","unstructured":"Rizkiah M, Widiaty I, Mulyanti B. Fashion pattern maker of software application development. IOP Conf Ser Mater Sci Eng. 2020;830(4):042096\u20139.","DOI":"10.1088\/1757-899X\/830\/4\/042096"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_032","doi-asserted-by":"crossref","unstructured":"Souza BP, Pereira AC. Not all path is straight: Limits and possibilities for the sensibilization of fashion design students through pattern making teaching. Univ do Estado de St Catarina. 2020;4(2):237\u201342.","DOI":"10.5965\/25944630422020237"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_033","doi-asserted-by":"crossref","unstructured":"Tsolmonchimeg B, Ahn SJ. Fashion Jewelry Design Inspired by Batik Pattern. J Korean Tradit Costume. 2019;22(2):103\u201318.","DOI":"10.16885\/jktc.2019.06.22.2.103"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_034","doi-asserted-by":"crossref","unstructured":"Cagnoni S, Dobrazeniecki AB, Poli R. Genetic algorithm-based interactive segmentation of 3D medical images. Image Vis Comput. 2019;17(12):881\u201395.","DOI":"10.1016\/S0262-8856(98)00166-8"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_035","doi-asserted-by":"crossref","unstructured":"Brintrup AM, Ramsden J, Tiwari A. An interactive genetic algorithm-based framework for handling qualitative criteria in design optimization. Computers Ind. 2017;58(3):279\u201391.","DOI":"10.1016\/j.compind.2006.06.004"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_036","doi-asserted-by":"crossref","unstructured":"Xue X, Yang C, Mao G. Semi-automatic ontology matching based on interactive compact genetic algorithm. Int J Pattern Recognit Artif Intell. 2022;36(5):124\u20139.","DOI":"10.1142\/S0218001422570026"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_037","doi-asserted-by":"crossref","unstructured":"Carrasco J, Garc\u00eda S, Rueda MM, Das S, Herrera F. Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review. Swarm Evolut Comput. 2020;54:100665.","DOI":"10.1016\/j.swevo.2020.100665"},{"key":"2025120517251264029_j_jisys-2023-0269_ref_038","doi-asserted-by":"crossref","unstructured":"Derrac J, Garc\u00eda S, Hui S, Suganthan PN, Herrera F. Analyzing convergence performance of evolutionary algorithms: A statistical approach. Inf Sci. 2014;289:41\u201358.","DOI":"10.1016\/j.ins.2014.06.009"}],"container-title":["Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2023-0269\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2023-0269\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T17:33:13Z","timestamp":1764955993000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/jisys-2023-0269\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,1]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,4,20]]},"published-print":{"date-parts":[[2024,4,20]]}},"alternative-id":["10.1515\/jisys-2023-0269"],"URL":"https:\/\/doi.org\/10.1515\/jisys-2023-0269","relation":{},"ISSN":["2191-026X"],"issn-type":[{"value":"2191-026X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,1]]},"article-number":"20230269"}}