初始化
计算机科学
特征选择
核(代数)
选择(遗传算法)
人工智能
特征(语言学)
人口
全局优化
数学优化
机器学习
模式识别(心理学)
算法
数学
语言学
哲学
人口学
组合数学
社会学
程序设计语言
作者
Honggang Yu,Zihang Zhao,Ali Asghar Heidari,Ling Ma,Monia Hamdi,Romany F. Mansour,Huiling Chen
出处
期刊:iScience
[Elsevier]
日期:2023-10-01
卷期号:26 (10): 107896-107896
标识
DOI:10.1016/j.isci.2023.107896
摘要
An improved whale optimization algorithm (SWEWOA) is presented for global optimization issues. Firstly, the sine mapping initialization strategy (SS) is used to generate the population. Secondly, the escape energy (EE) is introduced to balance the exploration and exploitation of WOA. Finally, the wormhole search (WS) strengthens the capacity for exploitation. The hybrid design effectively reinforces the optimization capability of SWEWOA. To prove the effectiveness of the design, SWEWOA is performed in two test sets, CEC 2017 and 2022, respectively. The advantage of SWEWOA is demonstrated in 26 superior comparison algorithms. Then a new feature selection method called BSWEWOA-KELM is developed based on the binary SWEWOA and kernel extreme learning machine (KELM). To verify its performance, 8 high-performance algorithms are selected and experimentally studied in 16 public datasets of different difficulty. The test results demonstrate that SWEWOA performs excellently in selecting the most valuable features for classification problems.
科研通智能强力驱动
Strongly Powered by AbleSci AI