.png){kind=icon}
返回
An improved linear prediction evolution algorithm based on nonlinear least square fitting model for optimization
.png){kind=icon}
OA
AI
DOI：10.1007/s00500-023-08500-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The linear prediction evolution algorithm (LPE) is a recent addition in the field of optimization algorithm with few parameters and high exploration capability. LPE has shown excellent results in some real-world problems but still suffers from some issues such as slow convergence speed. To improve the performance of the LPE algorithm, this study presents an improved linear prediction evolution algorithm (ILPE) to enhance its exploration capability. The proposed ILPE algorithm treats the population series of evolutionary algorithms as a time series and uses the nonlinear least square fitting model as a reproduction operator to forecast the next generation. The performance of the proposed ILPE is verified using the CEC2014 and CEC2017 benchmark functions. The comparison results show that ILPE outperforms LPE, and it is highly competitive with other state-of-the-art optimization algorithms.
Keyword:
Linear prediction evolution algorithm
Least square fitting
Meta-heuristics
Nonlinear optimization
期刊
IF:
2.5
论文数:
1.0W
被引数:
2.1W
机构
引用论文
分享收藏An Adaptive Island Evolutionary Algorithm for the berth scheduling problem.png)
MEMETIC COMPUTING
MEMETIC COMPUTINGIF2.3
PREAI
分享收藏
Four adaptive grey prediction evolution algorithms with different types of parameters setting techniquesSOFT COMPUTING
SOFT COMPUTINGIF2.5
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
查看更多内容