.png){kind=icon}
返回
A Grouping Cooperative Differential Evolution Algorithm for Solving Partially Separable Complex Optimization Problems
.png){kind=icon}
PRE
AI
DOI：10.1007/s12559-023-10128-5.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Differential evolution (DE) is a widely accepted optimization algorithm inspired by the mechanisms of biological evolution for complex optimization problems. In this paper, we put forward a co-evolutionary differential evolution (CDE) with a differential grouping (DG) mechanism to solve the complex optimization problems in which the variables are partially separable. In the CDE, a complex problem with coupled multivariable is decomposed by DG into certain independent sub-problems easy to be solved. Then polling, upper confidence bound (UCB), and random access are introduced to allocate search resources for multiple decoupled sub-problems, respectively. Finally, success-history-based parameter adaptation for differential evolution (SHADE) is adopted as a search engine to solve each sub-problem. The results of experiments on the CEC2017 show that CDE achieves a competitive search performance compared to other peer algorithms. This study suggests that the combination of DG strategy and polling method can effectively solve the optimization problem with partially separable variables.
Keyword:
Co-evolutionary differential evolution
Differential grouping
SHADE
Complex optimization problems
期刊
IF:
4.3
论文数:
1.6K
被引数:
3.6K
机构
引用论文
Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization
PREAI
分享收藏Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization求解全局数值优化问题的策略自适应差分进化算法
OAAI
OAAI分享收藏Transcranial direct current stimulation on prefrontal and parietal areas enhances motor imagery.png)
NeuroReport
NeuroReportIF0
PREAI
分享收藏
查看更多内容