Bare bones differential evolution
Bare bones differential evolution
复制标题
DOI：
10.1016/j.ejor.2008.02.035
复制
发表时间：
2009-07-01
影响因子：
6.4
通讯作者：
Salman, Ayed
中科院分区：
文献类型：
--
作者：
Omran, Mahamed G. H.;Engelbrecht, Andries P.;Salman, Ayed
准骨架差分进化算法是准骨架粒子群优化算法和差分进化算法相结合的一种新的、几乎无参数的优化算法。差分进化用于变异，对于每个粒子，与该粒子相关联的吸引子，定义为其个人和邻域最佳位置的加权平均值。该方法的性能进行了研究，并与差分进化，冯诺依曼粒子群优化算法和准骨架粒子群优化算法进行了比较。进行的实验表明，BBDE提供了极好的结果，具有很少的附加优势，几乎没有参数调整。此外，性能的骨干差分进化使用环和冯诺依曼邻域拓扑结构进行了研究。最后，研究了BBDE在现实世界的无监督图像分类问题中的应用。实验结果表明，所提出的方法相比，其他国家的最先进的聚类算法在所有衡量标准表现得非常好。(C)2008 Elsevier B.V.保留所有权利。
The barebones differential evolution (BBDE) is a new, almost parameter-free optimization algorithm that is a hybrid of the barebones particle swarm optimizer and differential evolution. Differential evolution is used to mutate, for each particle, the attractor associated with that particle, defined as a weighted average of its personal and neighborhood best positions. The performance of the proposed approach is investigated and compared with differential evolution, a Von Neumann particle swarm optimizer and a barebones particle swarm optimizer. The experiments conducted show that the BBDE provides excellent results with the added advantage of little, almost no parameter tuning. Moreover, the performance of the barebones differential evolution using the ring and Von Neumann neighborhood topologies is investigated. Finally, the application of the BBDE to the real-world problem of unsupervised image classification is investigated. Experimental results show that the proposed approach performs very well compared to other state-of-the-art clustering algorithms in all measured criteria. (C) 2008 Elsevier B.V. All rights reserved.