This chapter 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 dynamics that fuses the advantages of evolutionary computation and neural dynamics methods is proposed, which follows a metaheuristic 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 metaheuristic 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 dynamics in solving the nonconvex time-varying optimization problems.