改进的AEA算法及其在参数估计中的应用

群搜索算法(Group Search Optimizer，GSO)是一种新的群智能优化算法，适宜于解决多极值高维度优化问题，但其在优化的后期由于种群多样性不够，容易陷入局部最优。对GSO算法进行了改进，将文化算法的模型运用到GSO算法中，并引入群体适应度方差的概念来判断是否进行影响函数操作以提高收敛效率。将该算法与遗传算法(GA)、粒子群算法(PSO)和基本的GSO算法进行优化测试函数的对比实验，并将其运用于丁烷化工业过程中效益最大化问题的研究，所得结果均验证了改进算法的有效性。

An Improved AEA Algorithm and Its Application to Parameter Estimation

Group Search Optimizer(GSO) is a new swarm intelligence algorithm, which has a superior performance on high dimensional and multi model problems. However, due to the lower diversity of the population, GSO algorithm easily falls into the local optimum in the late optimization. An improved GSO based on the framework of cultural algorithm is presented in this paper. Moreover, the colony fitness variance is introduced to decide whether to undergo the operation of influence function such that the convergence efficiency can be arisen. The comparison experimentations with GA, PSO and GSO are made, and the improved GSO algorithm is also applied to the optimization problem of the profit in the butane alkylation process. These results show the effectiveness of the improved algorithm.