.png){kind=icon}
返回
Differential Evolution with Grid-Based Parameter Adaptation
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-015-1911-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The reduction of human intervention in tuning metaheuristic optimization algorithms has been an ongoing research pursuit. Differential Evolution is a very popular algorithm that counts a large number of variants. However, its efficiency has been shown to depend on the type of its crossover operators (binomial or exponential), mutation operators, as well as on the two parameters that dominate these procedures. Making proper decisions on these parameters has proved to be a laborious, problem-dependent task. We propose a parameter adaptation technique that allows the algorithm to dynamically determine the most suitable crossover type and parameter values during its execution. The technique is based on a search procedure in the discretized parameter search space, using estimations of the algorithm's performance. The proposed approach is tested and statistically validated on an established high-dimensional test suite. Also, comparisons with other algorithms are reported, verifying the competitiveness of the proposed approach.
Keyword:
Metaheuristic Optimization
Dynamic Parameter Adaptation
Parameter Control
Differential Evolution
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems大规模连续优化问题的进化算法和其他元启发式算法的编辑可伸缩性.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏分享收藏Scalability of generalized adaptive differential evolution for large-scale continuous optimizationSOFT COMPUTING
SOFT COMPUTINGIF2.5
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A MOS-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability testSOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏
查看更多内容