Collaborative Neural Solution for Time-Varying Nonconvex Optimization With Noise Rejection
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE(2024)
摘要
This 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.
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关键词
Optimization,Collaboration,Linear programming,Robustness,Heuristic algorithms,Convergence,Vectors,Time-varying nonconvex optimization problem,neural dynamics,noise rejection,manipulability optimization,redundant manipulator
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