• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2023, Vol. 45 ›› Issue (05): 940-950.

• 人工智能与数据挖掘 • 上一篇    

基于教与学和逐维柯西变异的鲸鱼优化算法

付接递1,2,李振东1,郭辉1   

  1. (1.宁夏大学信息工程学院,宁夏 银川 750021;2.郑州西亚斯学院电子信息工程学院,河南 郑州 451150)
  • 收稿日期:2022-09-02 修回日期:2022-10-31 接受日期:2023-05-25 出版日期:2023-05-25 发布日期:2023-05-17
  • 基金资助:
    宁夏自然科学基金(2021AAC03035,2021AAC03117);宁夏重点研发计划(引才专项)(2021BEB04069)

A whale optimization algorithm based on teaching and learning and dimensional Cauchy mutation

FU Jie-di1,2,LI Zhen-dong1,GUO Hui1   

  1. (1.School of Information Engineering,Ningxia University,Yinchuan 750021;
    2.School of Electronic Information Engineering,Zhengzhou Sias University,Zhengzhou 451150,China)
  • Received:2022-09-02 Revised:2022-10-31 Accepted:2023-05-25 Online:2023-05-25 Published:2023-05-17

摘要: 基本鲸鱼优化算法在面对复杂优化问题时仍然存在易陷入局部极值、收敛速度慢和计算精度低等问题,为此提出一种基于教与学和逐维柯西变异的鲸鱼优化算法TCWOA。首先,选用Sobol序列对鲸鱼种群进行初始化操作,可使种群分布更均匀;其次,引入教与学算法中的教学策略替换鲸鱼优化算法中的随机搜索策略,避免搜索的盲目性,提高算法的收敛速度;再次,采用带惯性权重的逐维柯西变异对鲸鱼最优个体进行变异扰动,助其跳出局部最优解,增强算法的全局搜索能力;最后,与多种优化算法在10个标准测试函数上的对比分析,以及用TCWOA先优化BP网络参数,再预测波士顿房价的应用研究结果,表明了该优化算法的有效性和准确性。

关键词: 鲸鱼优化算法, 教与学优化算法, 逐维柯西变异, TCWOA-BP模型预测

Abstract: In the face of complex optimization problems, the basic whale optimization algorithm still has problems such as easy to fall into local extremum, slow convergence speed and low calculation accuracy. Therefore, a whale optimization algorithm based on teaching and learning and dimensional Cauchy mutation, named TCWOA, is proposed. Firstly, Sobol sequence is selected to initialize the whale population, which can make the population distribution more uniform. Secondly, the teaching strategy in the teaching and learning algorithm is introduced to replace the random search strategy, avoiding the blindness of search and improving its convergence speed. Thirdly, the dimensional Cauchy mutation with inertia weight is used to perturb the whale's optimal individual, which can make it jump out of the local optimal solution and enhance the global search ability of the algorithm. Finally, the comparative analysis of various optimization algorithms on 10 standard test functions ware carried out. In the application research, TCWOA algorithm first optimize BP network parameters and then predicts Boston housing prices. The results verify the effectiveness and accuracy of the optimization algorithm. 


Key words: whale optimization algorithm, teaching and learning algorithm, dimensional Cauchy mutation, TCWOA-BP model prediction