中文摘要：

提出了一种基于AEA算法处理约束问题的自适应惩罚函数法。该算法通过统计迭代种群中个体对每个约束条件违反的次数,判定各约束的强弱地位,动态自适应地调整各个约束的惩罚系数,对于强约束给予较大的惩罚系数。同时对目标函数做出了相适应区分修改,使得可行解和不可行解的目标函数值出现一定的区分,目标函数项和惩罚项趋于平衡,避免了惩罚力度过大或过小,有利于算法前期快速进入可行解区域,后期寻找最满意解。通过标准测试函数试验结果与DE＋AMP、SSaDE算法进行比较,表明了提出的方法具有良好的适用性以及全局优化性能,将该方法应用于丁烯烷化过程的约束优化,取得了令人满意的结果。

英文摘要：

A new adaptive penalty function method based on the AEA algorithm was proposed to handle the constrained problems in this paper.It can determine the intensity of each constrain by counting the times of breaching its constrained condition in iterative population and adjust the punishment coefficient of various constraints dynamically and adaptively.The greater punishment coefficient will be given to a stronger constraint.In addition,the objective function is made a change accordingly,so that it contributes to a distinguishing between feasible solution and unfeasible solution of the objective function values.The improvements of objective function and penalty are helpful for balancing them and avoiding the penalty is too large or too small.The proposed algorithm would quickly enter the feasible solution area in earlier stage,and find the most satisfying solution later.The comparisons with the results of standard test function obtained by using DE＋AMP and SSaDE algorithms show that the proposed method has good applicability and global optimization performance.Furthermore,the proposed method was applied to the process optimization of butane alkylation and satisfactory result was achieved.