{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T18:07:22Z","timestamp":1787508442922,"version":"build-2736575974"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T00:00:00Z","timestamp":1706572800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T00:00:00Z","timestamp":1706572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Despite recent advancements in super-resolution neural network optimization, a fundamental challenge remains unresolved: as the number of parameters is reduced, the network\u2019s performance significantly deteriorates. This paper presents a novel framework called the Depthwise Separable Convolution Super-Resolution Neural Network Framework (DWSR) for optimizing super-resolution neural network architectures. The depthwise separable convolutions are introduced to reduce the number of parameters and minimize the impact on the performance of the super-resolution neural network. The proposed framework uses the RUNge Kutta optimizer (RUN) variant (MoBRUN) as the search method. MoBRUN is a multi-objective binary version of RUN, which balances multiple objectives when optimizing the neural network architecture. Experimental results on publicly available datasets indicate that the DWSR framework can reduce the number of parameters of the Residual Dense Network (RDN) model by 22.17% while suffering only a minor decrease of 0.018 in Peak Signal-to-Noise Ratio (PSNR), the framework can reduce the number of parameters of the Enhanced SRGAN (ESRGAN) model by 31.45% while losing only 0.08 PSNR. Additionally, the framework can reduce the number of parameters of the HAT model by 5.38% while losing only 0.02 PSNR.<\/jats:p>","DOI":"10.1007\/s10462-023-10648-4","type":"journal-article","created":{"date-parts":[[2024,1,30]],"date-time":"2024-01-30T15:02:06Z","timestamp":1706626926000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["DWSR: an architecture optimization framework for adaptive super-resolution neural networks based on meta-heuristics"],"prefix":"10.1007","volume":"57","author":[{"given":"Shu-Chuan","family":"Chu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-Chao","family":"Dou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeng-Shyang","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingping","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V\u00e1clav","family":"Sn\u00e1\u0161el","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junzo","family":"Watada","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,30]]},"reference":[{"key":"10648_CR1","doi-asserted-by":"crossref","unstructured":"Agustsson, E, Timofte, R. Ntire 2017 challenge on single image super-resolution: Dataset and study. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"10648_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115079","volume":"181","author":"I Ahmadianfar","year":"2021","unstructured":"Ahmadianfar I, Heidari AA, Gandomi AH, Chu X, Chen H (2021) Run beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method. Expert Syst Appl 181:115079","journal-title":"Expert Syst Appl"},{"key":"10648_CR3","doi-asserted-by":"crossref","unstructured":"Ahn N, Kang B, Sohn K-A (2018) Fast, accurate, and lightweight super-resolution with cascading residual network. In: Proceedings of the European Conference on Computer Vision (ECCV)","DOI":"10.1109\/CVPRW.2018.00123"},{"key":"10648_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107351","volume":"106","author":"B Akay","year":"2021","unstructured":"Akay B, Karaboga D, Gorkemli B, Kaya E (2021) A survey on the artificial bee colony algorithm variants for binary, integer and mixed integer programming problems. Appl Soft Comput 106:107351","journal-title":"Appl Soft Comput"},{"key":"10648_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105887","volume":"86","author":"MAH Akhand","year":"2020","unstructured":"Akhand MAH, Ayon SI, Shahriyar SA, Siddique N, Adeli H (2020) Discrete spider monkey optimization for travelling salesman problem. Appl Soft Comput 86:105887","journal-title":"Appl Soft Comput"},{"key":"10648_CR6","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1016\/j.ins.2019.10.029","volume":"512","author":"Z Beheshti","year":"2020","unstructured":"Beheshti Z (2020) A time-varying mirrored s-shaped transfer function for binary particle swarm optimization. Info Sci 512:1503\u20131542","journal-title":"Info Sci"},{"key":"10648_CR7","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M, Roumy, A, Guillemot, C, Alberi-Morel, M.L. Low-complexity single-image super-resolution based on nonnegative neighbor embedding (2012)","DOI":"10.5244\/C.26.135"},{"key":"10648_CR8","doi-asserted-by":"crossref","unstructured":"Bulat A, Yang J, Tzimiropoulos, G (2018) To learn image super-resolution, use a gan to learn how to do image degradation first. In: Proceedings of the European Conference on Computer Vision (ECCV)","DOI":"10.1007\/978-3-030-01231-1_12"},{"key":"10648_CR9","volume-title":"The numerical analysis of ordinary differential equations: Runge-Kutta and General Linear Methods","author":"JC Butcher","year":"1987","unstructured":"Butcher JC (1987) The numerical analysis of ordinary differential equations: Runge-Kutta and General Linear Methods. Wiley-Interscience, Hoboken"},{"key":"10648_CR10","doi-asserted-by":"crossref","unstructured":"Chen, X, Wang, X, Zhou, J, Qiao, Y, Dong, C. Activating more pixels in image super-resolution transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 22367\u201322377 (2023)","DOI":"10.1109\/CVPR52729.2023.02142"},{"issue":"4","key":"10648_CR11","first-page":"9","volume":"21","author":"S-C Chu","year":"2005","unstructured":"Chu S-C, Roddick JF, Pan J-S (2005) A parallel particle swarm optimization algorithm with communication strategies. J Info Sci Eng 21(4):9","journal-title":"J Info Sci Eng"},{"key":"10648_CR12","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1007\/978-3-540-36668-3_94","volume-title":"PRICAI 2006: trends in artificial intelligence","author":"S-C Chu","year":"2006","unstructured":"Chu S-C, Tsai P-w, Pan J-S (2006) Cat swarm optimization. In: Yang Q, Webb G (eds) PRICAI 2006: trends in artificial intelligence. Springer, Berlin, pp 854\u2013858"},{"key":"10648_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108124","volume":"241","author":"S-C Chu","year":"2022","unstructured":"Chu S-C, Xu X-W, Yang S-Y, Pan J-S (2022) Parallel fish migration optimization with compact technology based on memory principle for wireless sensor networks. Knowledge-Based Syst 241:108124","journal-title":"Knowledge-Based Syst"},{"key":"10648_CR14","doi-asserted-by":"crossref","unstructured":"Chu X, Zhang B, Xu R, Ma H (2019) Multi-objective reinforced evolution in mobile neural architecture search. arXiv preprint arXiv:1901.01074","DOI":"10.1007\/978-3-030-66823-5_6"},{"key":"10648_CR15","first-page":"1","volume-title":"Simulated annealing: from basics to applications","author":"D Delahaye","year":"2019","unstructured":"Delahaye D, Chaimatanan S (2019) Mongeau, M. In: Gendreau M, Potvin J-Y (eds) Simulated annealing: from basics to applications. Springer, Cham, pp 1\u201335"},{"key":"10648_CR16","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1007\/978-3-319-10593-2_13","volume-title":"Computer Vision - ECCV 2014","author":"C Dong","year":"2014","unstructured":"Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer Vision - ECCV 2014. Springer, Cham, pp 184\u2013199"},{"issue":"1","key":"10648_CR17","first-page":"1997","volume":"20","author":"T Elsken","year":"2019","unstructured":"Elsken T, Metzen JH, Hutter F (2019) Neural architecture search: a survey. J Mach Learn Res 20(1):1997\u20132017","journal-title":"J Mach Learn Res"},{"key":"10648_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2021.101022","volume":"69","author":"Y He","year":"2022","unstructured":"He Y, Zhang F, Mirjalili S, Zhang T (2022) Novel binary differential evolution algorithm based on taper-shaped transfer functions for binary optimization problems. Swarm Evol Comput 69:101022","journal-title":"Swarm Evol Comput"},{"issue":"6","key":"10648_CR19","doi-asserted-by":"publisher","first-page":"508","DOI":"10.1109\/TASSP.1978.1163154","volume":"26","author":"H Hou","year":"1978","unstructured":"Hou H, Andrews H (1978) Cubic splines for image interpolation and digital filtering. IEEE Trans Acoust Speech Signal Process 26(6):508\u2013517","journal-title":"IEEE Trans Acoust Speech Signal Process"},{"key":"10648_CR20","unstructured":"Howard, A.G, Zhu, M, Chen, B, Kalenichenko, D, Wang, W, Weyand, T, Andreetto, M, Adam, H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. CoRR abs\/1704.04861 (2017)"},{"key":"10648_CR21","doi-asserted-by":"crossref","unstructured":"Huang J-B, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2015.7299156"},{"issue":"5","key":"10648_CR22","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1166\/jmihi.2020.3035","volume":"10","author":"M Kalaiarasu","year":"2020","unstructured":"Kalaiarasu M, Anitha J (2020) Analysis of micro array gene expression data using multi-objective binary particle swarm optimization with fuzzy weighted clustering (mobpso-fwc) technique. J Medical Imaging Health Info 10(5):1049\u20131056","journal-title":"J Medical Imaging Health Info"},{"issue":"6","key":"10648_CR23","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","volume":"29","author":"R Keys","year":"1981","unstructured":"Keys R (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoustics Speech Signal Process 29(6):1153\u20131160","journal-title":"IEEE Trans Acoustics Speech Signal Process"},{"issue":"3","key":"10648_CR24","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.3390\/app11031092","volume":"11","author":"S Kim","year":"2021","unstructured":"Kim S, Jun D, Kim B-G, Lee H, Rhee E (2021) Single image super-resolution method using CNN-based lightweight neural networks. Appl Sci 11(3):1092","journal-title":"Appl Sci"},{"key":"10648_CR25","doi-asserted-by":"crossref","unstructured":"Kim J, Lee JK, Lee KM (2015) Accurate image super-resolution using very deep convolutional networks. CoRR abs\/1511.04587","DOI":"10.1109\/CVPR.2016.182"},{"key":"10648_CR26","doi-asserted-by":"crossref","unstructured":"Lai, W.-S, Huang, J.-B, Ahuja, N, Yang, M.-H. Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.618"},{"key":"10648_CR27","doi-asserted-by":"crossref","unstructured":"Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z, Shi W (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2017.19"},{"key":"10648_CR28","unstructured":"Li W, Lu X, Qian S, Lu J, Zhang X, Jia J (2021) On efficient transformer-based image pre-training for low-level vision. arXiv preprint arXiv:2112.10175"},{"key":"10648_CR29","doi-asserted-by":"crossref","unstructured":"Liang J, Cao J, Sun G, Zhang K, Van\u00a0Gool L, Timofte R (2021) Swinir: Image restoration using swin transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 1833\u20131844","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"10648_CR30","doi-asserted-by":"crossref","unstructured":"Lim B, Son S, Kim H, Nah S, Mu\u00a0Lee K (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops","DOI":"10.1109\/CVPRW.2017.151"},{"key":"10648_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2020.113324","volume":"224","author":"H Liu","year":"2020","unstructured":"Liu H, Li Y, Duan Z, Chen C (2020) A review on multi-objective optimization framework in wind energy forecasting techniques and applications. Energy Convers Manag 224:113324","journal-title":"Energy Convers Manag"},{"issue":"8","key":"10648_CR32","doi-asserted-by":"publisher","first-page":"5981","DOI":"10.1007\/s10462-022-10147-y","volume":"55","author":"H Liu","year":"2022","unstructured":"Liu H, Ruan Z, Zhao P, Dong C, Shang F, Liu Y, Yang L, Timofte R (2022) Video super-resolution based on deep learning: a comprehensive survey. Artif Intell Rev 55(8):5981\u20136035","journal-title":"Artif Intell Rev"},{"key":"10648_CR33","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/978-3-030-58452-8_3","volume-title":"Computer Vision - ECCV 2020","author":"Z Lu","year":"2020","unstructured":"Lu Z, Deb K, Goodman E, Banzhaf W, Boddeti VN (2020) Nsganetv2: evolutionary multi-objective surrogate-assisted neural architecture search. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds) Computer Vision - ECCV 2020. Springer, Cham, pp 35\u201351"},{"key":"10648_CR34","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.chemolab.2015.08.020","volume":"149","author":"F Marini","year":"2015","unstructured":"Marini F, Walczak B (2015) Particle swarm optimization (pso) a tutorial. Chemom Intell Lab Syst 149:153\u2013165","journal-title":"Chemom Intell Lab Syst"},{"key":"10648_CR35","doi-asserted-by":"crossref","unstructured":"Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, vol. 2, pp. 416\u20134232","DOI":"10.1109\/ICCV.2001.937655"},{"key":"10648_CR36","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.knosys.2019.01.006","volume":"168","author":"Z Meng","year":"2019","unstructured":"Meng Z, Pan J-S, Tseng K-K (2019) Pade: an enhanced differential evolution algorithm with novel control parameter adaptation schemes for numerical optimization. Knowledge-Based Syst 168:80\u201399","journal-title":"Knowledge-Based Syst"},{"key":"10648_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.swevo.2012.09.002","volume":"9","author":"S Mirjalili","year":"2013","unstructured":"Mirjalili S, Lewis A (2013) S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm Evol Comput 9:1\u201314","journal-title":"Swarm Evol Comput"},{"key":"10648_CR38","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/978-981-15-3290-0_19","volume-title":"Soft computing for problem solving 2019","author":"S Mirjalili","year":"2020","unstructured":"Mirjalili S, Zhang H, Mirjalili S, Chalup S, Noman N (2020) A novel u-shaped transfer function for binary particle swarm optimisation. In: Nagar AK, Deep K, Bansal JC, Das KN (eds) Soft computing for problem solving 2019. Springer, Singapore, pp 241\u2013259"},{"key":"10648_CR39","first-page":"1","volume":"224","author":"V Mishra","year":"2022","unstructured":"Mishra V, Kane L (2022) A survey of designing convolutional neural network using evolutionary algorithms. Artif Intell Rev 224:1\u201338","journal-title":"Artif Intell Rev"},{"key":"10648_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107745","volume":"111","author":"D Moldovan","year":"2021","unstructured":"Moldovan D, Slowik A (2021) Energy consumption prediction of appliances using machine learning and multi-objective binary grey wolf optimization for feature selection. Appl Soft Comput 111:107745","journal-title":"Appl Soft Comput"},{"key":"10648_CR41","doi-asserted-by":"crossref","unstructured":"Morales-Hern\u00e1ndez A, Van\u00a0Nieuwenhuyse I, Rojas\u00a0Gonzalez S (2022) A survey on multi-objective hyperparameter optimization algorithms for machine learning. Artif Intell Rev, 1\u201351","DOI":"10.1007\/s10462-022-10359-2"},{"key":"10648_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106342","volume":"93","author":"A Rodr\u00edguez-Molina","year":"2020","unstructured":"Rodr\u00edguez-Molina A, Mezura-Montes E, Villarreal-Cervantes MG, Aldape-P\u00e9rez M (2020) Multi-objective meta-heuristic optimization in intelligent control: a survey on the controller tuning problem. Appl Soft Comput 93:106342","journal-title":"Appl Soft Comput"},{"issue":"1","key":"10648_CR43","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1038\/s41556-018-0251-8","volume":"21","author":"L Schermelleh","year":"2019","unstructured":"Schermelleh L, Ferrand A, Huser T, Eggeling C, Sauer M, Biehlmaier O, Drummen GP (2019) Super-resolution microscopy demystified. Nat Cell Biol 21(1):72\u201384","journal-title":"Nat Cell Biol"},{"key":"10648_CR44","doi-asserted-by":"crossref","unstructured":"Shi W, Caballero J, Husz\u00e1r F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016) Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1874\u20131883","DOI":"10.1109\/CVPR.2016.207"},{"issue":"07","key":"10648_CR45","first-page":"12007","volume":"34","author":"D Song","year":"2020","unstructured":"Song D, Xu C, Jia X, Chen Y, Xu C, Wang Y (2020) Efficient residual dense block search for image super-resolution. Proc AAAI Conf Artif Intell 34(07):12007\u201312014","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"10648_CR46","doi-asserted-by":"crossref","unstructured":"Tai Y, Yang J, Liu X (2017) Image super-resolution via deep recursive residual network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2017.298"},{"issue":"8","key":"10648_CR47","first-page":"1","volume":"54","author":"Y Tian","year":"2021","unstructured":"Tian Y, Si L, Zhang X, Cheng R, He C, Tan KC, Jin Y (2021) Evolutionary large-scale multi-objective optimization: a survey. ACM Comput Surv 54(8):1\u201334","journal-title":"ACM Comput Surv"},{"key":"10648_CR48","doi-asserted-by":"crossref","unstructured":"Timofte, R, Agustsson, E, Van\u00a0Gool, L, Yang, M.-H, Zhang, L. Ntire 2017 challenge on single image super-resolution: Methods and results. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2017)","DOI":"10.1109\/CVPRW.2017.150"},{"key":"10648_CR49","doi-asserted-by":"crossref","unstructured":"Tong, T, Li, G, Liu, X, Gao, Q. Image super-resolution using dense skip connections. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.514"},{"issue":"6","key":"10648_CR50","doi-asserted-by":"publisher","first-page":"777","DOI":"10.3390\/e24060777","volume":"24","author":"GL Wang","year":"2022","unstructured":"Wang GL, Chu SC, Tian AQ, Liu T, Pan JS (2022) Improved binary grasshopper optimization algorithm for feature selection problem. Entropy 24(6):777","journal-title":"Entropy"},{"key":"10648_CR51","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1016\/j.ins.2014.04.013","volume":"279","author":"H Wang","year":"2014","unstructured":"Wang H, Wu Z, Rahnamayan S, Sun H, Liu Y, Pan J-S (2014) Multi-strategy ensemble artificial bee colony algorithm. Info Sci 279:587\u2013603","journal-title":"Info Sci"},{"key":"10648_CR52","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1016\/j.ins.2022.12.117","volume":"626","author":"P Wang","year":"2023","unstructured":"Wang P, Xue B, Liang J, Zhang M (2023) Feature selection using diversity-based multi-objective binary differential evolution. Info Sci 626:586\u2013606","journal-title":"Info Sci"},{"key":"10648_CR53","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.swevo.2019.06.002","volume":"49","author":"Y Wang","year":"2019","unstructured":"Wang Y, Zhang H, Zhang G (2019) CPSO-CNN: an efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional neural networks. Swarm Evolut Comput 49:114\u2013123","journal-title":"Swarm Evolut Comput"},{"key":"10648_CR54","doi-asserted-by":"crossref","unstructured":"Wang X, Yu K, Wu S, Gu J, Liu Y, Dong C, Qiao Y, Change\u00a0Loy C (2018) Esrgan: Enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV) Workshops","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"10648_CR55","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07985-w","author":"S Yacoubi","year":"2022","unstructured":"Yacoubi S, Manita G, Amdouni H, Mirjalili S, Korbaa O (2022) A modified multi-objective slime mould algorithm with orthogonal learning for numerical association rules mining. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-022-07985-w","journal-title":"Neural Comput Appl"},{"key":"10648_CR56","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/978-3-642-27413-8_47","volume-title":"Curves Surfaces","author":"R Zeyde","year":"2012","unstructured":"Zeyde R, Elad M, Protter M (2012) On single image scale-up using sparse-representations. In: Boissonnat J-D, Chenin P, Cohen A, Gout C, Lyche T, Mazure M-L, Schumaker L (eds) Curves Surfaces. Springer, Berlin, Heidelberg, pp 711\u2013730"},{"key":"10648_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103905","volume":"95","author":"X Zhang","year":"2020","unstructured":"Zhang X, Liu H, Tu L (2020) A modified particle swarm optimization for multimodal multi-objective optimization. Eng Appl Artif Intell 95:103905","journal-title":"Eng Appl Artif Intell"},{"key":"10648_CR58","doi-asserted-by":"crossref","unstructured":"Zhang Y, Tian Y, Kong Y, Zhong B, Fu Y (2018) Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR.2018.00262"},{"key":"10648_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.cma.2021.114029","volume":"385","author":"K Zhong","year":"2021","unstructured":"Zhong K, Zhou G, Deng W, Zhou Y, Luo Q (2021) MOMPA: multi-objective marine predator algorithm. Comput Methods Appl Mech Eng 385:114029","journal-title":"Comput Methods Appl Mech Eng"},{"key":"10648_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105139","volume":"114","author":"X Zhou","year":"2022","unstructured":"Zhou X, Ma H, Gu J, Chen H, Deng W (2022) Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism. Eng Appl Artif Intell 114:105139","journal-title":"Eng Appl Artif Intell"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10648-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-023-10648-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10648-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T07:09:55Z","timestamp":1708672195000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-023-10648-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,30]]},"references-count":60,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["10648"],"URL":"https:\/\/doi.org\/10.1007\/s10462-023-10648-4","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,30]]},"assertion":[{"value":"30 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest:"}}],"article-number":"23"}}