{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T13:12:24Z","timestamp":1771679544901,"version":"3.50.1"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,7,13]],"date-time":"2021-07-13T00:00:00Z","timestamp":1626134400000},"content-version":"vor","delay-in-days":193,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Youth Innovative Talents Project in Ordinary Universities of Guangdong Province","award":["2020KQNCX126"],"award-info":[{"award-number":["2020KQNCX126"]}]},{"name":"Youth Innovative Talents Project in Ordinary Universities of Guangdong Province","award":["2018KTSCX319"],"award-info":[{"award-number":["2018KTSCX319"]}]},{"name":"Characteristic Innovation Projects of Ordinary Universities in Guangdong Province","award":["2020KQNCX126"],"award-info":[{"award-number":["2020KQNCX126"]}]},{"name":"Characteristic Innovation Projects of Ordinary Universities in Guangdong Province","award":["2018KTSCX319"],"award-info":[{"award-number":["2018KTSCX319"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>The traditional IPv6 routing algorithm has problems such as network congestion, excessive energy consumption of nodes, and shortening the life cycle of the network. In response to this phenomenon, we proposed a routing optimization algorithm based on genetic ant colony in IPv6 environment. The algorithm analyzes and studies the genetic algorithm and the ant colony algorithm systematically. We use neural network to build the initial model and combine the constraints of QoS routing. We effectively integrate the genetic algorithm and ant colony algorithm that maximize their respective advantages and apply them to the IPv6 network. At the same time, in order to avoid the accumulation of a lot of pheromones by the ant colony algorithm in the later stage of the network, we have introduced an anticongestion reward and punishment mechanism. By comparing the search path with the optimal path, rewards and punishments are based on whether the network path is smooth or not. Finally, it is judged whether the result meets the condition, and the optimal solution obtained is passed to the BP neural network for training; otherwise, iterative iterations are required until the optimal solution is satisfied. The experimental results show that the algorithm can effectively adapt to the IPv6 routing requirements and can effectively solve the user\u2019s needs for network service quality, network performance, and other aspects.<\/jats:p>","DOI":"10.1155\/2021\/3115704","type":"journal-article","created":{"date-parts":[[2021,7,14]],"date-time":"2021-07-14T02:05:06Z","timestamp":1626228306000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["[Retracted] Neural Network Optimal Routing Algorithm Based on Genetic Ant Colony in IPv6 Environment"],"prefix":"10.1155","volume":"2021","author":[{"given":"Weichuan","family":"Ni","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiming","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajun","family":"Zou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiping","family":"Wan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4276-2527","authenticated-orcid":false,"given":"Xiaolei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,7,13]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/mcom.2002.1024413"},{"key":"e_1_2_11_2_2","first-page":"462","article-title":"A study on performance of hierarchical mobile IPv6 in IP-based cellular networks","volume":"87","author":"Sangheaon Pack A.","year":"2004","journal-title":"IEICE Transactions on Communications"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1023\/b:wire.0000047072.64159.35"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/4236.895146"},{"key":"e_1_2_11_5_2","article-title":"Routing of site-scoped addresses in the internet protocol version 6 (IPv6)","volume":"5","author":"Bharath N.","year":"1969","journal-title":"Molecular Pharmacology"},{"key":"e_1_2_11_6_2","first-page":"1155","article-title":"Raw IPv6 traffic generation and handling in simulated test environment","volume":"39","author":"Hirvel\u00e4 J.","year":"2011","journal-title":"Journal of International Medical Research"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00202-011-0196-4"},{"key":"e_1_2_11_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0890-6955(99)00063-2"},{"key":"e_1_2_11_9_2","first-page":"1","article-title":"Optimal selection and placement of point and nonpoint source pollution control strategies using a genetic algorithm","volume":"18","author":"Taheriyoun M.","year":"2009","journal-title":"International Agricultural Engineering Journal"},{"key":"e_1_2_11_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/0020-0255(94)90076-0"},{"key":"e_1_2_11_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/72.286889"},{"key":"e_1_2_11_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/tsmcb.2012.2221695"},{"key":"e_1_2_11_13_2","first-page":"1816","article-title":"A study on optimal routing methods of computer networks using neural networks","volume":"45","author":"Ouk J.","year":"1996","journal-title":"The Transactions of The Korean Institute of Electrical Engineers"},{"key":"e_1_2_11_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/9.887639"},{"key":"e_1_2_11_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(88)90183-9"},{"key":"e_1_2_11_16_2","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2019.05848"},{"key":"e_1_2_11_17_2","doi-asserted-by":"crossref","unstructured":"ReisJ. RochaM. PhanT. K.et al. Deep neural networks for network routing Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN) July 2019 Budapest Hungary https:\/\/doi.org\/10.1109\/IJCNN.2019.8851733 2-s2.0-85072801399.","DOI":"10.1109\/IJCNN.2019.8851733"},{"key":"e_1_2_11_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-21133-6_7"},{"key":"e_1_2_11_19_2","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2020.09176"},{"key":"e_1_2_11_20_2","doi-asserted-by":"publisher","DOI":"10.32604\/iasc.2020.012663"},{"key":"e_1_2_11_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmc.2012.204"},{"key":"e_1_2_11_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/9.272341"},{"key":"e_1_2_11_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/90.748088"},{"key":"e_1_2_11_24_2","unstructured":"LorenzD. H.andOrdaA. QoS routing in networks with uncertain parameters Proceedings of IEEE INFOCOM\u201998 the Conference on Computer Communications. 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