.png){kind=icon}
返回
Multi-strategy enhanced Marine Predators Algorithm with applications in engineering optimization and feature selection problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2024.111650.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper introduces a novel meta -heuristic optimization algorithm named Clustering Wavelet Oppositionbased Marine Predators Algorithm (CWOMPA) to address some limitations present in the well -established Marine Predators Algorithm (MPA). CWOMPA incorporates three key strategies: a fuzzy clustering approach for escaping local optima, using wavelet basis function -based impact coefficient adjustment for elites to prevent premature convergence, and finally an adaptive opposition -based learning strategy for maintaining population diversity. Compared with some recent meta -heuristic algorithms, extensive evaluations conducted affirm that CWOMPA achieves the best Friedman rank, 4.30 and 1.95 respectively, on 23 benchmark functions and the CEC 2017 benchmark set. Not only does CWOMPA demonstrate significant effectiveness on six constrained problems from the CEC 2020 real -world benchmarks, but also when applied to 14 medical datasets, it gains superior Friedman ranks in terms of selected features, classification accuracy, F -Score, and objective function value, 2.5, 2.25, 2.96, 2.93 respectively, outperforming other common methods. Finally, CWOMPA exhibits the highest classification accuracy across all datasets and the best F -Score performance in nine datasets compared to traditional feature selection algorithms. The obtained results reveal that CWOMPA is a powerful and versatile optimization algorithm with significant potential in various real -world applications, including feature selection for medical datasets.
Keyword:
Marine predators algorithm
Fuzzy clustering
Wavelet basis function
Opposition-based learning
Constrained optimization problems
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
New hybrid nature-based algorithm to integration support vector machine for prediction of soil cation exchange capacity基于混合自然的新算法集成支持向量机预测土壤阳离子交换量.png)
SOFT COMPUTING
OAAI
SOFT COMPUTINGIF2.5
OAAI分享收藏分享收藏
Multi-strategy boosted marine predators algorithm for optimizing approximate developable surface
PREAI
分享收藏
查看更多内容