乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T16:43:05Z","timestamp":1743612185402,"version":"3.37.3"},"reference-count":56,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1833128"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1016\/j.knosys.2023.110470","type":"journal-article","created":{"date-parts":[[2023,3,11]],"date-time":"2023-03-11T21:25:53Z","timestamp":1678569953000},"page":"110470","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":12,"special_numbering":"C","title":["An improved differential evolution algorithm based on basis vector type and its application in fringe projection 3D imaging"],"prefix":"10.1016","volume":"268","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6247-7056","authenticated-orcid":false,"given":"Xuxu","family":"Zhong","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5884-9267","authenticated-orcid":false,"given":"Peng","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Zhisheng","family":"You","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2023.110470_b1","article-title":"Fitness-distance balance (FDB): A new selection method for meta-heuristic search algorithms","volume":"190","author":"Kahraman","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2023.110470_b2","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Global Optim."},{"key":"10.1016\/j.knosys.2023.110470_b3","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","article-title":"Harris hawks optimization: Algorithm and applications","volume":"97","author":"Heidari","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.knosys.2023.110470_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.cma.2020.113609","article-title":"The arithmetic optimization algorithm","volume":"376","author":"Abualigah","year":"2021","journal-title":"Comput. Methods Appl. Mech. Engrg."},{"key":"10.1016\/j.knosys.2023.110470_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2021.107250","article-title":"Aquila optimizer: A novel meta-heuristic optimization algorithm","volume":"157","author":"Abualigah","year":"2021","journal-title":"Comput. Ind. Eng."},{"key":"10.1016\/j.knosys.2023.110470_b6","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.matcom.2021.08.013","article-title":"Honey badger algorithm: New metaheuristic algorithm for solving optimization problems","volume":"192","author":"Hashim","year":"2022","journal-title":"Math. Comput. Simulation"},{"key":"10.1016\/j.knosys.2023.110470_b7","article-title":"A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean","volume":"389","author":"Chou","year":"2021","journal-title":"Appl. Math. Comput."},{"key":"10.1016\/j.knosys.2023.110470_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.advengsoft.2020.102804","article-title":"Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems","volume":"146","author":"Das","year":"2020","journal-title":"Adv. Eng. Softw."},{"key":"10.1016\/j.knosys.2023.110470_b9","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.future.2020.03.055","article-title":"Slime mould algorithm: A new method for stochastic optimization","volume":"111","author":"Li","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.knosys.2023.110470_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2019.105190","article-title":"Equilibrium optimizer: A novel optimization algorithm","volume":"191","author":"Faramarzi","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2023.110470_b11","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s00521-015-1870-7","article-title":"Multi-verse optimizer: a nature-inspired algorithm for global optimization","volume":"27","author":"Mirjalili","year":"2015","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.knosys.2023.110470_b12","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.advengsoft.2015.01.010","article-title":"The ant lion optimizer","volume":"83","author":"Mirjalili","year":"2015","journal-title":"Adv. Eng. Softw."},{"key":"10.1016\/j.knosys.2023.110470_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2020.113474","article-title":"A hybrid adaptive teaching-learning-based optimization and differential evolution for parameter identification of photovoltaic models","volume":"225","author":"Li","year":"2020","journal-title":"Energy Convers. Manage."},{"key":"10.1016\/j.knosys.2023.110470_b14","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No free lunch theorems for optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.knosys.2023.110470_b15","series-title":"2017 IEEE Congress on Evolutionary Computation (CEC)","article-title":"Real-world applications in the communications industry - when do we resort to differential evolution?","author":"Storn","year":"2017"},{"key":"10.1016\/j.knosys.2023.110470_b16","first-page":"1","article-title":"UAV stocktaking task-planning for industrial warehouses based on improved hybrid differential evolution algorithm","volume":"99","author":"Liu","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.knosys.2023.110470_b17","article-title":"Performance evaluation of non-uniform circular antenna array using integrated harmony search with differential evolution based naked mole rat algorithm","volume":"189","author":"Singh","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2023.110470_b18","article-title":"Optimized differential evolution algorithm for solving DEM material calibration problem","author":"Ji","year":"2022","journal-title":"Eng. Comput."},{"key":"10.1016\/j.knosys.2023.110470_b19","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1109\/TEVC.2009.2014613","article-title":"JADE: Adaptive differential evolution with optional external archive","volume":"13","author":"Zhang","year":"2009","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.knosys.2023.110470_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2019.112443","article-title":"An enhanced adaptive differential evolution algorithm for parameter extraction of photovoltaic models","volume":"205","author":"Li","year":"2020","journal-title":"Energy Convers. Manage."},{"key":"10.1016\/j.knosys.2023.110470_b21","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.asoc.2014.01.038","article-title":"Differential evolution based on covariance matrix learning and bimodal distribution parameter setting","volume":"18","author":"Wang","year":"2014","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.knosys.2023.110470_b22","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.ins.2017.02.055","article-title":"Differential evolution powered by collective information","volume":"399","author":"Zheng","year":"2017","journal-title":"Inform. Sci."},{"key":"10.1016\/j.knosys.2023.110470_b23","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1007\/s13042-017-0711-7","article-title":"Adaptive guided differential evolution algorithm with novel mutation for numerical optimization","volume":"10","author":"Mohamed","year":"2019","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.knosys.2023.110470_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107421","article-title":"Fitness-distance balance based adaptive guided differential evolution algorithm for security-constrained optimal power flow problem incorporating renewable energy sources","volume":"108","author":"Guvenc","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.knosys.2023.110470_b25","article-title":"A clustering-based differential evolution with different crowding factors for nonlinear equations system","volume":"98","author":"Wu","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.knosys.2023.110470_b26","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1016\/j.ins.2015.09.009","article-title":"Differential evolution with multi-population based ensemble of mutation strategies","volume":"329","author":"Wu","year":"2016","journal-title":"Inform. Sci."},{"key":"10.1016\/j.knosys.2023.110470_b27","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.ins.2017.09.002","article-title":"Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism","volume":"422","author":"Cui","year":"2018","journal-title":"Inform. Sci."},{"key":"10.1016\/j.knosys.2023.110470_b28","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.renene.2020.12.131","article-title":"Equilibrium optimizer for parameter extraction of a fuel cell dynamic model","volume":"169","author":"Seleem","year":"2021","journal-title":"Renew. Energy"},{"key":"10.1016\/j.knosys.2023.110470_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114766","article-title":"Opposition-based Laplacian equilibrium optimizer with application in image segmentation using multilevel thresholding","volume":"174","author":"Dinkar","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2023.110470_b30","first-page":"466","article-title":"Deep learning in optical metrology: a review","volume":"11","author":"Zuo","year":"2022","journal-title":"Light: Sci. Appl."},{"key":"10.1016\/j.knosys.2023.110470_b31","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.swevo.2019.02.004","article-title":"A hierarchical gravitational search algorithm with an effective gravitational constant","volume":"46","author":"Wang","year":"2019","journal-title":"Swarm Evol. Comput."},{"key":"10.1016\/j.knosys.2023.110470_b32","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.ins.2009.03.004","article-title":"GSA: A gravitational search algorithm","volume":"179","author":"Rashedi","year":"2009","journal-title":"Inform. Sci."},{"key":"10.1016\/j.knosys.2023.110470_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.113377","article-title":"Marine predators algorithm: A nature-inspired metaheuristic","volume":"152","author":"Faramarzi","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.knosys.2023.110470_b34","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.swevo.2015.10.007","article-title":"Self-adaptive control parameters randomization frequency and propagations in differential evolution","volume":"25","author":"Zamuda","year":"2015","journal-title":"Swarm Evol. Comput."},{"key":"10.1016\/j.knosys.2023.110470_b35","article-title":"A better balance in metaheuristic algorithms: Does it exist?","volume":"54","author":"Morales-Castaeda","year":"2020","journal-title":"Swarm Evol. Comput."},{"key":"10.1016\/j.knosys.2023.110470_b36","doi-asserted-by":"crossref","unstructured":"J. Derrac, S. Garc\u00eda, D. Molina, F. Herrera, A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm Evol. Comput. 1 (2011) 3\u201318, http:\/\/dx.doi.org\/10.1016\/j.swevo.2011.02.002.","DOI":"10.1016\/j.swevo.2011.02.002"},{"key":"10.1016\/j.knosys.2023.110470_b37","article-title":"Real-time three-dimensional imaging technique based on phase-shift fringe analysis: A review","volume":"58","author":"Guo","year":"2021","journal-title":"Laser Optoelectron. Prog."},{"key":"10.1016\/j.knosys.2023.110470_b38","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.knosys.2023.110470_b39","unstructured":"Barret Zoph, Quoc Le, Neural Architecture Search with Reinforcement Learning, in: ICLR 2017 Conference, http:\/\/dx.doi.org\/10.48550\/arXiv.1611.01578."},{"key":"10.1016\/j.knosys.2023.110470_b40","series-title":"18th International Conference on Medical Image Computing and Computer-Assisted Intervention","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.knosys.2023.110470_b41","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: IEEE Conference on Computer Vision and Pattern Recognition, 2016, https:\/\/arxiv.org\/abs\/1512.03385.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.knosys.2023.110470_b42","unstructured":"S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International Conference on Machine Learning, 2015, https:\/\/arxiv.org\/abs\/1502.03167."},{"key":"10.1016\/j.knosys.2023.110470_b43","first-page":"195","article-title":"The influence of the sigmoid function parameters on the speed of backpropagation learning","volume":"930","author":"Han","year":"2005","journal-title":"Nat. Artif. Neural Comput."},{"key":"10.1016\/j.knosys.2023.110470_b44","unstructured":"I. Loshchilov, F. Hutter, SGDR: Stochastic Gradient Descent with Warm Restarts, in: International Conference on Learning Representations, ICLR, 2017, https:\/\/arxiv.org\/abs\/1608.03983."},{"key":"10.1016\/j.knosys.2023.110470_b45","unstructured":"D. Kingma, J. Ba, Adam: A Method for Stochastic Optimization, in: International Conference on Learning Representations, ICLR, 2015, https:\/\/arxiv.org\/abs\/1412.6980."},{"key":"10.1016\/j.knosys.2023.110470_b46","unstructured":"V. Nair, G.E. Hinton, Rectified Linear Units Improve Restricted Boltzmann Machines, in: International Conference on International Conference on Machine Learning, 2010."},{"key":"10.1016\/j.knosys.2023.110470_b47","unstructured":"A.L. Maas, A.Y. Hannun, A.Y. Ng, Rectifier nonlinearities improve neural network acoustic models, in: International Conference on Machine Learning, 2013, https:\/\/ai.stanford.edu\/amaas\/papers\/relu_hybrid_icml2013_final.pdf."},{"key":"10.1016\/j.knosys.2023.110470_b48","article-title":"Empirical evaluation of rectified activations in convolutional network","author":"Xu","year":"2015","journal-title":"Comput. Sci."},{"key":"10.1016\/j.knosys.2023.110470_b49","unstructured":"A. Krizhevsky, Convolutional Deep Belief Networks on CIFAR-10. http:\/\/www.cs.utoronto.ca\/~kriz\/conv-cifar10-aug2010.pdf."},{"key":"10.1016\/j.knosys.2023.110470_b50","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neunet.2017.12.012","article-title":"Sigmoid-weighted linear units for neural network function approximation in reinforcement learning","volume":"107","author":"Elfwing","year":"2018","journal-title":"Neural Netw."},{"key":"10.1016\/j.knosys.2023.110470_b51","article-title":"Mish: A self regularized non-monotonic activation function","author":"Misra","year":"2019","journal-title":"Comput. Sci."},{"key":"10.1016\/j.knosys.2023.110470_b52","article-title":"Fast and accurate deep network learning by exponential linear units (ELUs)","author":"Clevert","year":"2015","journal-title":"Comput. Sci."},{"key":"10.1016\/j.knosys.2023.110470_b53","article-title":"Self-normalizing neural networks","author":"G\u00fcnte","year":"2017","journal-title":"Comput. Sci."},{"key":"10.1016\/j.knosys.2023.110470_b54","article-title":"Gaussian error linear units (GELUs)","author":"Hendrycks","year":"2016","journal-title":"Comput. Sci."},{"key":"10.1016\/j.knosys.2023.110470_b55","unstructured":"V. Nair, G.E. Hinton, Rectified linear units improve restricted boltzmann machines, in: International Conference on Machine Learning, 2010."},{"key":"10.1016\/j.knosys.2023.110470_b56","doi-asserted-by":"crossref","unstructured":"J. Turian, J. Bergstra, Y. Bengio, Quadratic features and deep architectures for chunking, in: North American Chapter of the Association for Computational Linguistics, 2009,.","DOI":"10.3115\/1620853.1620921"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705123002204?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705123002204?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,2,10]],"date-time":"2024-02-10T22:06:17Z","timestamp":1707602777000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705123002204"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5]]},"references-count":56,"alternative-id":["S0950705123002204"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2023.110470","relation":{},"ISSN":["0950-7051"],"issn-type":[{"type":"print","value":"0950-7051"}],"subject":[],"published":{"date-parts":[[2023,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An improved differential evolution algorithm based on basis vector type and its application in fringe projection 3D imaging","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2023.110470","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"110470"}}