The gradient-based methods can converge to an optimal or near-optimal solution quickly, but they are not efficient in non-differentiable problems. Additionally, the gradient search may rely on the position of an initial point if there are various local optimums in the problem. Furthermore, the search result may become unstable when the objective function and constraints have sharp peaks. The drawbacks of traditional numerical methods have forced researchers to focus on meta-heuristic algorithms.