<p>This paper presents an optimization method based on optimal control for finding multiple local minimum points of non-convex objective functions. The authors reformulate the original optimization problem (OP) as an optimal control problem and solve it using numerical algorithms to obtain the state trajectory of the system, which approaches the local minimum point of the original objective function in a fast and stable way. The proposed method is capable of identifying multiple local minimum points within a single optimization process and escaping saddle points by strategically designing the initial control sequence in numerical algorithms. Furthermore, the convexity of the optimal control problem can be ensured by choosing the control weight matrix, providing a novel perspective for solving non-convex OPs. Finally, the authors demonstrate that the proposed method exhibits high accuracy, low oscillation, and more stability in non-convex settings, highlighting its practicality and potential in tackling complex optimization tasks.</p>