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多种群纵横双向学习和信息互换的鲸鱼优化算法

刘小龙

刘小龙. 多种群纵横双向学习和信息互换的鲸鱼优化算法[J]. 电子与信息学报. doi: 10.11999/JEIT201080
引用本文: 刘小龙. 多种群纵横双向学习和信息互换的鲸鱼优化算法[J]. 电子与信息学报. doi: 10.11999/JEIT201080
Xiaolong LIU. Whale Optimization Algorithm for Multi-group with Information Exchange and Vertical and Horizontal Bidirectional Learning[J]. Journal of Electronics and Information Technology. doi: 10.11999/JEIT201080
Citation: Xiaolong LIU. Whale Optimization Algorithm for Multi-group with Information Exchange and Vertical and Horizontal Bidirectional Learning[J]. Journal of Electronics and Information Technology. doi: 10.11999/JEIT201080

多种群纵横双向学习和信息互换的鲸鱼优化算法

doi: 10.11999/JEIT201080
基金项目: 中央高校基本科研业务费(XYZD201911)
详细信息
    作者简介:

    刘小龙:男,1977年生,讲师,研究方向为仿生优化与计算智能

    通讯作者:

    刘小龙 xlliu@scut.edu.cn

  • 中图分类号: TP301.6

Whale Optimization Algorithm for Multi-group with Information Exchange and Vertical and Horizontal Bidirectional Learning

Funds: The Fundamental Research Funds for the Central University (XYZD201911)
  • 摘要: 鲸鱼优化算法(WOA)相较于传统的群体智能优化算法,具有较好的寻优能力和鲁棒性,但仍存在全局寻优能力有限、局部极值难以跳出等问题。针对上述不平衡问题,该文提出一种多种群纵横双向学习的种群划分思路,子群相互独立,子群内个体受到来自横向和纵向两个方向的最优值影响,从而规避局部最优,在探索和开发之间取得均衡。对纵向种群的所有个体,该文提出一种线性下降概率的个体置换策略,促进不同子群的信息流动,加快算法收敛。基于不同个体的历史进化信息,来进行策略算子选择,从而区别于现有基于随机数的策略算子选择方法。利用基准函数进行跨文献对比,数值结果表明该文算法具有很好的优越性和稳定性,在大多数问题上都获得了全局极值,具有较好的问题适用性。
  • 图  1  改进鲸鱼优化算法的多种群纵横双向结构

    表  1  本文算法参数CPp的基准函数测试均值精度等级(D=30)

    实验F1F2F3F4F5F6F7F8F9F10F11F12F13
    10e-1670e-169e-004e-009e-004–125690e-0160e-010e-009
    2e-323e-1680e-16700e-004–123320e-0160e-032e-032
    30e-1670e-16900e-004–123310e-0160e-032e-032
    40e-1680e-16400e-004–122140e-0160e-032e-032
    5e-055e-019e-011e-06600e-004–103471.98e-0160e-032e-032
    下载: 导出CSV

    表  2  本文算法参数A的基准函数测试均值精度等级(D=30)

    实验F1F2F3F4F5F6F7F8F9F10F11F12F13
    60e-1940e-19200e-004–117400e-0160e-032e-032
    70e-2180e-2180.95690e-004–119770e-0160e-032e-032
    80e-2290e-22600e-004–117932.984e-0160e-032e-032
    下载: 导出CSV

    表  3  本文算法种群参数pk的基准函数测试均值精度等级(D=30)

    实验F1F2F3F4F5F6F7F8F9F10F11F12F13
    900002.870e-004–110268.954e-0160e-032e-032
    100e-1640e-16800e-004–119771.989e-0160e-032e-032
    11e-264e-133e-260e-13400e-004–125690e-0160e-032e-032
    12e-214e-108e-212e-10100e-004–125690e-0160e-032e-032
    下载: 导出CSV

    表  4  针对基本测试函数的算法性能均值指标对比(D=30)

    F1F2F3F4F5F6F7F8F9F10F11F12F13
    本算法e-157e-81e-153e-8100e-04–125690e-160e-32e-32
    HHOe-97e-51e-63e-47e-02e-04e-04e+040e-160e-06e-04
    WOAe-30e-21e-07e-0227.863.11e-03–508007.40e-040.3391.889
    下载: 导出CSV

    表  5  本文算法与现有文献的性能指标对比(D=30)

    W-SA-WOA[13]CWOA[14]EGolden-SWOA[15]IMWOA[16]本文算法
    平均值标准差平均值标准差平均值标准差平均值标准差平均值标准差
    F1000000002.7e-1571.4e-156
    F22.57e-216.68e-1214.56e-22306.69e-20208.82e-18103.8e-0819.1e-81
    F30000001.2e-1536.8e-153
    F43.56e-944.90e-943.6e-26503.58e-19104.27e-18401.6e-0813.0e-081
    F527.33570.29560.2745.17e-003.75e-097.45e-094.29e-051.33e-0400
    F60.02710.0201006.86e-101.29e-090000
    F71.17e-041.0E-043.61e-053.73e-053.25e-052.55e-05001.4e-041.25e-04
    F8–12447873.3422–5.58e-1011.67e + 102–12455.6172.0869–125691.8e-12
    F90000000000
    F103.02e-151.77e-158.88e-0164.01e-318.88e-1608.88e-0161.00e-0318.88e-0160
    F110.00150.007300000000
    F120.07852.40e-083.09e-021.37e-028.53e-142. 61e-101.68e-081.70e-081.5e-0325.5e-48
    F130.04210.02892.63e-116.65e-105.69e-061.96e-051.3e-0325.5e-48
    下载: 导出CSV

    表  6  大规模(D=1000)测试函数的算法性能指标对比

    RosenbrockPenalized1
    对比算法均值标准差成功率均值标准差成功率
    本文算法 0 0 100 4.7116e-034 68.6991e-050 100
    文献[11] 1.38e-17 4.2e-17 100 4.13e-28 0 100
    文献[12] 9.92e+02 8.29e-01 0 1.59e-01 6.19e-02 0
    文献[14] 9.90e+02 4.51e-01 0 3.46e-02 1.76e-02 3.33
    文献[16] 2.66e-08 4.96e-08 100 3.14e-09 4.24e-09 100
    文献[24] 0.3318 0.3973 10 2.20e-06 3.56e-06 100
    下载: 导出CSV

    表  7  本文算法与文献[1223]的性能对比(D=1000)

    函数IWOA[12]GWOA[23]本文算法
    均值标准差成功率均值标准差成功率均值标准差成功率
    f11.86e-1119.0e-1121001.60e-1145.01e-1141008.7516e-1572.707e-156100
    f23.00e-0653.37e-0651001.56e-0732.09e-0731007.2131e-0801.7043e-079100
    f33.15e-1126.20e-1121009.59e-1142.03e-1131004.0063e-1532.1768e-152100
    f43.00e-18201004.29e-16801001.4807e-1618.1032e-161100
    f59.92e+028.29e-0109.88e+ 026.18e-004000100
    f63.06e-0032.52e-003202.07e-0023.53e-002302.2722e-041.7054e-04100
    f71.10e-0012.25e-002401.05e-0073.14e-0071004.7116e-0348.6991e-050100
    f8001000010000100
    f95.15e-0152.89e-0151008.88e-01601008.8818e-0160100
    f10001000010000100
    f111.02e-0661.63e-066100001001.0549e-0765.7776e-076100
    f126.58e-1121.30e-1111006.32e-1221.58e-1221001.3948e-1226.6809e-122100
    f13001000010000100
    f141.66e-1153.30e-1151003.64e-1304.58e-1301001.3498e-1305.5674e-130100
    f155.52e-1031.20e-1021003.33e-1051.05e-1041002.2266e-1539.0116e-153100
    下载: 导出CSV
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  • 收稿日期:  2020-12-25
  • 修回日期:  2021-03-12
  • 网络出版日期:  2021-03-24

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