.png){kind=icon}
返回
PaDE: An enhanced Differential Evolution algorithm with novel control parameter adaptation schemes for numerical optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2019.01.006.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) variants have been proven to be excellent algorithms in tackling real parameter single objective numerical optimization because they have secured the front ranks of these competitions for many years. Nevertheless, there are still some weaknesses, e.g. (1) improper control parameter adaptation schemes; and (2) defect in a given mutation strategy., existing in some state-of-the-art DE variants, which may result in slow convergence and worse optimization performance. Therefore, in this paper, a novel Parameter adaptive DE (PaDE) is proposed to tackle the above mentioned weaknesses and the PaDE algorithm has three advantages: (1) A grouping strategy with novel adaptation scheme for Cr is proposed to tackle the improper adaptation schemes of Cr in some state-of-the-art DE variants; (2) A novel parabolic population size reduction scheme is proposed to tackle the weakness in linear population size reduction scheme; (3) An enhanced time stamp based mutation strategy is proposed to tackle the weakness in a former mutation strategy. The novel PaDE algorithm is verified under 58 benchmarks from two Congress on Evolutionary Computation (CEC) Competition test suites on real-parameter single objective numerical optimization, and experiment results show that the proposed PaDE algorithm is competitive with the other state-of-the-art DE variants. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Control parameters
Differential evolution
Numerical optimization
Population size reduction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
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
.png){kind=icon}
OAAI分享收藏Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
PREAI
分享收藏Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters具有复合试验向量生成策略和控制参数的差分进化
PREAI
分享收藏分享收藏分享收藏
查看更多内容