摘要： 针对社会认知优化算法中知识点的更新过程遍历性不强，不利于快速获得最优解的问题，通过引入一维Kent映射函数和混沌因子，对算法中的邻域搜索过程进行优化和改进，使得更新的知识点数值更合理，分布更均匀。实验数据表明，使用该社会认知优化算法在求解非线性问题时，较遗传算法和标准社会认知优化算法收敛速度更快，准确率更高，函数目标值更接近理论值。

关键词: 进化计算, 社会认知优化, Kent映射函数, 混沌, 非线性约束

Abstract: Focusing on the problem of the process of refreshing, the knowledge point is so weak that it is bad for getting the optimization resolution in social cognitive algorithm. Through bringing in the chaos and Kent mapping function to modify and optimize the conditions of neighborhood search, it can get more reasonable knowledge points which are distributed more uniformly. Using the compared data, it can find that the speed of convergence and the legitimacy is better than before, and the value of the object function is closed to the theory value.

Key words: evolutionary computation, social cognitive optimization, Kent mapping function, chaos, nonlinear constraint

中图分类号: 

• TP301.6