Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (13): 74-84.DOI: 10.3778/j.issn.1002-8331.2110-0192

• Theory, Research and Development • Previous Articles     Next Articles

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



  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.

Key words: improved whale optimization algorithm, center wander, boundary neighborhood updates, double weight factors, engineering optimization

摘要: 针对鲸鱼优化算法(WOA)收敛精度低、收敛速度慢、易陷入局部优化等问题,提出耦合中心游移和双权重因子的鲸鱼算法(C-A-WWOA)。该算法采用中心游移和边界邻域更新策略,提高了种群质量、收敛精度和收敛速度;通过算法参数的非线性改进,平衡了算法的局部开发与全局搜索能力;还采用双权重因子对后期种群进行随机扰动,以避免算法后期陷入局部最优。通过18个测试函数的计算表明,相较于WOA和其他改进方案,C-A-WWOA在没有增加算法复杂度的基础上,提高了收敛精度和适用性。同时,不同改进策略下对算法性能的影响排序为:C-A-WWOA>W-WOA>C-WOA≈A-WOA>WOA;此外,改进算法在两个工程结构设计实例的应用中,也验证了其有效性和优越性。

关键词: 改进鲸鱼优化算法, 中心游移, 边界邻域更新, 双权重因子, 工程优化