Collaborative Neural Solution for Time-Varying Nonconvex Optimization With Noise Rejection
Lin Wei, Long Jin
Abstract
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.