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However, it remains unclear how their visual response mechanisms can be borrowed to construct neurocomputational models for solving optimization problems. Hereby, a feedforward dragonfly visual attention\u2013merged neural network (DVAMNN) with presynaptic and postsynaptic subnetworks is developed to output two types of online activities named learning rates in terms of the dragonfly visual information\u2010processing and attention mechanisms. Integrated such learning rates into a new\u2010type and metaheuristics\u2010inspired state transition strategy, a dragonfly visual attention\u2013merged evolutionary neural network (DVAMENN) with the unique parameter of input resolution is developed to solve ultrahigh dimensional global optimization (UHDGO) problems. The theoretical analysis implicates that the DVAMENN\u2019s complexity is mainly decided by the optimization problem itself. Experimental results have confirmed that DVAMENN can successfully optimize the structures of two sixth\u2010order active filters and discover the global or approximate solutions of the CEC\u2019 2010 and CEC\u2019 2013 benchmark suites with dimension 20,000 per example. Nevertheless, the compared metaheuristics encounter unprecedented troubles in the case of UHDGO.<\/jats:p>","DOI":"10.1155\/int\/6614031","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T18:28:15Z","timestamp":1761762495000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dragonfly Visual Attention\u2013Merged Evolutionary Neural Network Solving Ultrahigh Dimensional Global Optimization Problems"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7846-9075","authenticated-orcid":false,"given":"Heng","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9093-5691","authenticated-orcid":false,"given":"Zhuhong","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,10,29]]},"reference":[{"key":"e_1_2_13_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCI.2017.2666551"},{"key":"e_1_2_13_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.12.021"},{"key":"e_1_2_13_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.113151"},{"key":"e_1_2_13_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2021.3091619"},{"key":"e_1_2_13_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106640"},{"key":"e_1_2_13_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00158-020-02613-4"},{"key":"e_1_2_13_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ast.2021.106639"},{"key":"e_1_2_13_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2020.2964027"},{"key":"e_1_2_13_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.conengprac.2019.104232"},{"key":"e_1_2_13_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2983768"},{"key":"e_1_2_13_11_2","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2111.05685"},{"key":"e_1_2_13_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2018.02.001"},{"key":"e_1_2_13_13_2","article-title":"Particle Swarm Optimization Algorithm for Solving Large-Scale Function Optimization","volume":"42","author":"Xiao T. 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