Abstract: Concerning the fact that the objective function of a class of nonlinear minimax problems is non-smooth caused difficulty in solving this problem，a new algorithm is proposed.This algorithm uses social cognitive optimization algorithm with maximum entropy function method.Firstly，the maximum entropy function is used to transform the minimax problems into unconstrained differentiable optimization problem，then using the social cognitive optimization algorithm to solve this problem.The algorithm is based on social cognitive theory，through a series of learning agents to simulate human social and intelligent thereby completing the optimization of the target.The numerical results show that the algorithm converges faster and has numerical stability，and it is an effective algorithm for nonlinear minimax problems.

Key words: social cognitive optimization, nonlinear minimax problems, maximum-entropy method

摘要： 针对一类非线性极大极小问题目标函数非光滑的特点给求解带来的困难，利用社会认知算法并结合极大熵函数法给出了此类问题的一种新的有效算法。首先利用极大熵函数将原问题转化为一个光滑无约束优化问题，然后利用社会认知算法对其进行求解。该算法是基于社会认知理论，通过一系列的学习代理来模拟人类的社会性以及智能性从而完成对目标的优化。数值结果表明，该算法收敛快，数值稳定性好，是求解非线性极大极小问题的一种有效算法。

关键词: 社会认知算法, 极大极小问题, 极大熵方法