.png){kind=icon}
返回
An Adaptive Differential Evolution Algorithm for Global Optimization in Dynamic Environments
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2013.2278188.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This article proposes a multipopulation-based adaptive differential evolution (DE) algorithm to solve dynamic optimization problems (DOPs) in an efficient way. The algorithm uses Brownian and adaptive quantum individuals in conjunction with the DE individuals to maintain the diversity and exploration ability of the population. This algorithm, denoted as dynamic DE with Brownian and quantum individuals (DDEBQ), uses a neighborhood-driven double mutation strategy to control the perturbation and thereby prevents the algorithm from converging too quickly. In addition, an exclusion rule is used to spread the subpopulations over a larger portion of the search space as this enhances the optima tracking ability of the algorithm. Furthermore, an aging mechanism is incorporated to prevent the algorithm from stagnating at any local optimum. The performance of DDEBQ is compared with several state-of-the-art evolutionary algorithms using a suite of benchmarks from the generalized dynamic benchmark generator (GDBG) system used in the competition on evolutionary computation in dynamic and uncertain environments, held under the 2009 IEEE Congress on Evolutionary Computation (CEC). The simulation results indicate that DDEBQ outperforms other algorithms for most of the tested DOP instances in a statistically meaningful way.
Keyword:
Differential evolution
diversity
double mutation strategy
dynamic optimization problems
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Real-parameter evolutionary multimodal optimization - A survey of the state-of-the-art实参数进化多模态优化-最新技术综述
PREAI
分享收藏Improving copper stress tolerance in Mentha suaveolens L. by foliar application of salicylic acid.png)
IF0
PREAI
分享收藏A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏
分享收藏
查看更多内容