Yu Helong, Zhao Zisong, Heidari Ali Asghar, Ma Li, Hamdi Monia, Mansour Romany F, Chen Huiling
College of Information Technology, Jilin Agricultural University, Changchun 130118, China.
Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China.
iScience. 2023 Sep 14;26(10):107896. doi: 10.1016/j.isci.2023.107896. eCollection 2023 Oct 20.
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.
针对全局优化问题，提出了一种改进的鲸鱼优化算法（SWEWOA）。首先，采用正弦映射初始化策略（SS）来生成种群。其次，引入逃逸能量（EE）以平衡鲸鱼优化算法的探索和开发能力。最后，虫洞搜索（WS）增强了开发能力。这种混合设计有效地增强了SWEWOA的优化能力。为了证明该设计的有效性，分别在CEC 2017和2022这两个测试集上对SWEWOA进行了测试。在26种高级比较算法中证明了SWEWOA的优势。然后基于二进制SWEWOA和核极限学习机（KELM）开发了一种名为BSWEWOA-KELM的新特征选择方法。为了验证其性能，选择了8种高性能算法，并在16个不同难度的公共数据集上进行了实验研究。测试结果表明，SWEWOA在为分类问题选择最有价值的特征方面表现出色。
