• Theory, Research and Development •

### Whale Algorithm Based on Coupled Center Wander and Double Weight Factors and Its Applications

CHENG Haomiao, WANG Menglei, WANG Liang, ZHANG Xiaowei

1. 1.School of Environmental Science and Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, China
2.School of Hydraulic Science and Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, China
3.School of Information Engineering, Yangzhou University, Yangzhou, Jiangsu 225127, China
• Online:2022-07-01 Published:2022-07-01

### 耦合中心游移和双权重因子的鲸鱼算法及应用

1. 程浩淼，王梦磊，汪靓，章小卫3
1.扬州大学 环境科学与工程学院，江苏 扬州 225127
2.扬州大学 水利科学与工程学院，江苏 扬州 225127
3.扬州大学 信息工程学院，江苏 扬州 225127

Abstract: An improved whale optimization algorithm based on the coupled center wander and double weight factors（C-A-WWOA） is proposed to solve the problems of low convergence precision, slow convergence rate and easily falling into local optimal solution. Firstly, the center wander and boundary neighborhood updates are used to improve the quality of population, convergence precision and rate. Then, a nonlinear improvement of parameters is put forward to balance local development and global search capability. Finally, two different weight factors are introduced for the stochastic perturbation of population to avoid the local optimal solution. The simulation results of 18 standard test functions show that the C-A-WWOA has higher convergence precision and wider applicability without algorithmic complexity penalty, comparing with whale optimization algorithm（WOA） and other improved WOA in previous studies. Meanwhile, the optimization effects of improvement strategies in C-A-WWOA are followed by C-A-WWOA>W-WOA >C-WOA ≈A-WOA>WOA. In addition, the effectiveness and superiority of the C-A-WWOA are verified via two structural design problems.