Department of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700 032, India.
IEEE Trans Cybern. 2013 Jun;43(3):881-97. doi: 10.1109/TSMCB.2012.2217491. Epub 2012 Oct 18.
This paper presents a Cluster-based Dynamic Differential Evolution with external Archive (CDDE_Ar) for global optimization in dynamic fitness landscape. The algorithm uses a multipopulation method where the entire population is partitioned into several clusters according to the spatial locations of the trial solutions. The clusters are evolved separately using a standard differential evolution algorithm. The number of clusters is an adaptive parameter, and its value is updated after a certain number of iterations. Accordingly, the total population is redistributed into a new number of clusters. In this way, a certain sharing of information occurs periodically during the optimization process. The performance of CDDE_Ar is compared with six state-of-the-art dynamic optimizers over the moving peaks benchmark problems and dynamic optimization problem (DOP) benchmarks generated with the generalized-dynamic-benchmark-generator system for the competition and special session on dynamic optimization held under the 2009 IEEE Congress on Evolutionary Computation. Experimental results indicate that CDDE_Ar can enjoy a statistically superior performance on a wide range of DOPs in comparison to some of the best known dynamic evolutionary optimizers.
本文提出了一种基于聚类的具有外部档案的动态差分进化算法（CDDE_Ar），用于动态适应度景观中的全局优化。该算法采用多群体方法，根据试验解的空间位置将整个群体划分为若干个聚类。聚类使用标准差分进化算法分别进化。聚类的数量是一个自适应参数，在经过一定数量的迭代后会进行更新。相应地，总群体将重新分配到新的聚类数量中。通过这种方式，在优化过程中周期性地发生一定程度的信息共享。将 CDDE_Ar 的性能与六个最先进的动态优化器在移动峰基准问题和使用广义动态基准生成器系统生成的动态优化基准问题（DOP）基准上进行了比较，这些基准问题是在 2009 年 IEEE 进化计算大会上举行的动态优化竞赛和特别会议上竞争的。实验结果表明，与一些最知名的动态进化优化器相比，CDDE_Ar 在广泛的 DOP 上具有统计学上优越的性能。
