中文摘要：

在这个工作，在 AEA (基于 Alopex 的进化算法) 的缺点上集中算法，是有同种细胞的选择算法的熔化 AEA 的一个改进 AEA 算法(AEA-C ) 被建议。就在 AEA 的每次重复产生了候选人答案的方法的荒唐而言，同种细胞的选择算法能被使用改进方法。建议新算法的表演被使用 22 基准功能学习并且与给一样的条件的原来的 AEA 相比。试验性的结果证明 AEA-C 清楚地与 10 为几乎所有 22 个基准函数超过原来的 AEA， 30，处于成功率，解决方案质量和稳定性的 50 种尺寸。而且， AEA-C 被使用估计发酵动力学模型的 6 个动力学参数。AEA-C 计算的客观功能的标准差是 41.46 并且是远的不到另外的文学结果的，和 AEA-C 获得的恰当的曲线是与实际发酵过程曲线一致的更多。

英文摘要：

In this work, focusing on the demerit of AEA （Alopex-based evolutionary algorithm） algorithm, an improved AEA algorithm （AEA-C） which was fused AEA with clonal selection algorithm was proposed. Considering the irrationality of the method that generated candidate solutions at each iteration of AEA, clonal selection algorithm could be applied to improve the method. The performance of the proposed new algorithm was studied by using 22 benchmark functions and was compared with original AEA given the same conditions. The experimental results show that the AEA-C clearly outperforms the original AEA for almost all the 22 benchmark functions with 10, 30, 50 dimensions in success rates, solution quality and stability. Furthermore, AEA-C was applied to estimate 6 kinetics parameters of the fermentation dynamics models. The standard deviation of the objective function calculated by the AEA-C is 41.46 and is far less than that of other literatures＇ results, and the fitting curves obtained by AEA-C are more in line with the actual fermentation process curves.