.png){kind=icon}
返回
Dispersed foraging slime mould algorithm: Continuous and binary variants for global optimization and wrapper-based feature selection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2021.107761.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The slime mould algorithm (SMA) is a logical swarm-based stochastic optimizer that is easy to understand and has a strong optimization capability. However, the SMA is not suitable for solving multimodal and hybrid functions. Therefore, in the present study, to enhance the SMA and maintain population diversity, a dispersed foraging SMA (DFSMA) with a dispersed foraging strategy is proposed. We conducted extensive experiments based on several functions in IEEE CEC2017. The DFSMA were compared with 11 other meta-heuristic algorithms (MAs), 10 advanced algorithms, and 3 recently proposed algorithms. Moreover, to conduct more systematic data analyses, the experimental results were further evaluated using the Wilcoxon signed-rank test. The DFSMA was shown to outperform other optimizers in terms of convergence speed and accuracy. In addition, the binary DFSMA (BDFSMA) was obtained using the transform function. The performance of the BDFSMA was evaluated on 12 datasets in the UCI repository. The experimental results reveal that the BDFSMA performs better than the original SMA, and that, compared with other optimization algorithms, it improves classification accuracy and reduces the number of selected features, demonstrating its practical engineering value in spatial search and feature selection. (c) 2021 Published by Elsevier B.V.
Keyword:
Slime mould algorithm
Swarm intelligence
Global optimization
Feature selection
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm蛾焰优化算法: 一种新的自然启发启发式范式
PREAI
分享收藏分享收藏Boosting slime mould algorithm for parameter identification of photovoltaic models用于光伏模型参数辨识的Boosting煤泥模算法.png)
ENERGY
ENERGYIF9.4
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
查看更多内容