{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T20:34:49Z","timestamp":1773002089753,"version":"3.50.1"},"reference-count":44,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62476115"],"award-info":[{"award-number":["62476115"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["CAAI Trans on Intel Tech"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Nonconvex optimisation plays a crucial role in science and industry. However, existing methods often encounter local optima or provide inferior solutions when solving nonconvex optimisation problems, lacking robustness in noise scenarios. To address these limitations, we aim to develop a robust, efficient and globally convergent solver for nonconvex optimisation. This is achieved by combining the efficient local exploitation ability of a parameter\u2010learnt neural dynamics (PLND) model with the global search capability of the coevolutionary mechanism. We combine their characteristics to design a coevolutionary neural dynamics with learnable parameters (CNDLP) model. The gradient information is used to find the optimal solution more effectively, and neural dynamics models have robustness, which ensures that the influence of noise can be effectively suppressed in the calculation process. Theoretical analyses show the global convergence and robustness of the designed CNDLP model. Numerical experiments on 9 benchmark functions and a practical engineering design example are conducted with five existing meta\u2010heuristic algorithms. Benchmarks cover diverse problems, from classical landscapes like benchmark Shubert to high\u2010dimensional cases such as 30\u2010dimensional Rosenbrock. Results confirm CNDLP's excellent performance in both solution quality and convergence speed under noise.<\/jats:p>","DOI":"10.1049\/cit2.70074","type":"journal-article","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T12:24:34Z","timestamp":1761654274000},"page":"111-122","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Coevolutionary Neural Dynamics With Learnable Parameters for Nonconvex Optimisation"],"prefix":"10.1049","volume":"11","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0003-3268-2114","authenticated-orcid":false,"given":"Yipiao","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Qinghai Normal University  Xining China"},{"name":"School of Cyber Security Gansu University of Political Science and Law  Lanzhou China"},{"name":"School of Information Science and Engineering Lanzhou University  Lanzhou China"}]},{"given":"Wenbin","family":"Du","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering Lanzhou University  Lanzhou China"},{"name":"School of Computer Science and Engineering Jishou University  Jishou China"}]},{"given":"Huichao","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering Lanzhou University of Technology  Lanzhou China"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-5329-5098","authenticated-orcid":false,"given":"Long","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Computer Qinghai Normal University  Xining China"},{"name":"School of Information Science and Engineering Lanzhou University  Lanzhou China"},{"name":"School of Automation and Electrical Engineering Lanzhou University of Technology  Lanzhou China"}]}],"member":"265","published-online":{"date-parts":[[2025,10,28]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/tsmc.2023.3287237"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2023.3289712"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2021.3071764"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/tie.2024.3404155"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/tii.2023.3348830"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/tsmc.2023.3288224"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2021.3090204"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/tsp.2023.3244326"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2020.3017555"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/tsp.2023.3240652"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/tsp.2022.3185895"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2020.2984331"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2024.3353795"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2023.3309598"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2022.3163974"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.102464"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462\u2010024\u201011023\u20107"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/tevc.2021.3102863"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3159713"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2021.3125362"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1080\/21642583.2019.1708830"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2016.01.008"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2019.01.010"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICEESA.2013.6578397"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1080\/10407782.2013.832063"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3220806"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2022.3179312"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2003.817760"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2020.3041364"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2020.3009110"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3306374"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCSI.2003.817760"},{"key":"e_1_2_9_34_1","first-page":"6571","volume-title":"Advances in Neural Information Processing Systems","author":"Chen T. 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