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Collaborative Neural Solution for Time-Varying Nonconvex Optimization With Noise Rejection
L. Wei; L. Jin
2024-03-18
Source PublicationIEEE Transactions on Emerging Topics in Computational Intelligence   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
ISSN2471-285X
VolumePPIssue:99Pages:1-14
AbstractThis paper focuses on an emerging topic that current neural dynamics methods generally fail to accurately solve time-varying nonconvex optimization problems especially when noises are taken into consideration. A collaborative neural solution that fuses the advantages of evolutionary computation and neural dynamics methods is proposed, which follows a meta-heuristic rule and exploits the robust gradient-based neural solution to deal with different noises. The gradient-based neural solution with robustness (GNSR) is proven to converge with the disturbance of noises and experts in local search. Besides, theoretical analysis ensures that the meta-heuristic rule guarantees the optimal solution for the global search with probability one. Lastly, simulative comparisons with existing methods and an application to manipulability optimization on a redundant manipulator substantiate the superiority of the proposed collaborative neural solution in solving the nonconvex time-varying optimization problems.
KeywordTime-varying nonconvex optimization problem neural dynamics noise rejection manipulability optimization redundant manipulator
PublisherIEEE
DOI10.1109/TETCI.2024.3369482
Indexed ByIEEE
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