优化算法有很多，经典算法包括：有线性规划，动态规划等；改进型局部搜索算法包括爬山法，最速下降法等，模拟退火、遗传算法以及禁忌搜索称作指导性搜索法。而神经网络，混沌搜索则属于系统动态演化方法。

梯度为基础的传统优化算法具有较高的计算效率、较强的可靠性、比较成熟等优点，是一类最重要的、应用最广泛的优化算法。但是，传统的最优化方法在应用于复杂、困难的优化问题时有较大的局限性。(There are many optimization algorithms, the classical algorithms include linear programming, dynamic programming, etc. the improved local search algorithms include hill-climbing method, steepest descent method, etc. simulated annealing, genetic algorithm and tabu search are called the guiding search methods. The neural network and chaotic search belong to the dynamic evolution method of the system.

Gradient based traditional optimization algorithm has the advantages of high computational efficiency, strong reliability and relatively mature. It is one of the most important and most widely used optimization algorithms. However, the traditional optimization method has great limitations when it is applied to complex and difficult optimization problems.)