文章 "种群优化算法：微人工免疫系统（Micro-AIS）"

Andrey Dik 2024.01.20 21:54 #21

Vladimir Suslov #:

max = pi/2 + n*2*pi

其中 n 为任意整数

限制条件在哪里？

袋子的周期可以是负数吗？不可以，这是下面的限制条件。

飞轮的周期可以大于 10000 吗？可以，但没有意义，所以这是上面的限制条件。

等等。实际问题都有限制条件，这就是为什么它们是 N-完备的（当然不仅如此），否则应用优化算法就没有实际意义了--会花很长时间。

Vladimir Suslov 2024.01.20 21:59 #22

Andrey Dik #:

马赫周期可以是负数吗？

检查器的周期能否大于 10000？可以，但没有意义，这是上面的限制。

等等。实际问题都有限制条件，这就是为什么它们是 N-完备的（当然不仅如此）

实际上，我是在跟fxsaber 专门讨论他的 FF

，他不想用 mashka 来切换...

fxsaber

2024.01.20
www.mql5.com

Профиль трейдера

Andrey Dik 2024.01.20 22:11 #23

Vladimir Suslov #:

实际上，我是在和fxsaber 谈论他的 FF

，而不是想改用 mashki...

啊，对不起。继续说，抱歉打断了你们的谈话。

fxsaber 2024.01.20 22:49 #24

关于将优化技术应用于技术合作的问题。我很难想象同时优化十个以上输入的 TS 是合理的。

因此，TS 的 "自由度 "似乎不到十个。这包括对优化算法的一些要求--"酷 "不再是普遍性（优化具有大量输入的超级复杂事物）。

也就是说，对于 TC 来说，你需要了解你真正想从优化中得到什么。

Andrey Dik 2024.01.20 23:00 #25

fxsaber #:

关于将优化应用于 TC 的问题。我很难想象同时对十多个输入进行优化是合理的。

因此，TS 的 "自由度 "似乎不足一打。因此，对优化算法提出了一些要求--其酷炫之处不再是多功能性（优化具有大量输入的超级复杂事物）。

也就是说，对于 TC 来说，你需要了解你真正想从优化中得到什么。

超过 10 个参数的优化应用领域非常广泛。

首先是模式。

第二是实时投资组合。

第三--神经网络和组合、实时 "硬件"。

第四--现在能在屏幕后与我们对话的一切，不久都将变得更加智能，这要归功于拥有数千乃至数十亿个参数的自适应系统。

生命是通过优化氨基酸产生的，就像第一....。这也是优化，第一个通过全球优化测试的优化。

fxsaber 2024.01.20 23:31 #26

Andrey Dik #:

对 10 多个参数进行优化的应用领域非常广泛。

我说的是 TC。

Andrey Dik 2024.01.20 23:33 #27

fxsaber #:

我说的是 TC。

我也是。

fxsaber 2024.01.21 01:38 #28

Andrey Dik #:

某些类型的算法在使用多重复制（模拟多维性）的基准上可能会高估结果。

根据这种方法，可以使用 Hilly 函数进行测试。

#define  dInput01 X1
#define  dInput02 Y1
#define  dInput03 X2
#define  dInput04 Y2
#define  dInput05 X3
#define  dInput06 Y3

#include <fxsaber\Input_Struct\Input_Struct.mqh> //https://www.mql5.com/zh/code/47932

INPUT_STRUCT inInputs;

MACROS_INPUT(double, X1, 0);
MACROS_INPUT(double, Y1, 0);
MACROS_INPUT(double, X2, 0);
MACROS_INPUT(double, Y2, 0);
MACROS_INPUT(double, X3, 0);
MACROS_INPUT(double, Y3, 0);

#include <Math\Functions.mqh> //https://www.mql5.com/zh/articles/13951

double OnTester()
{
  static C_Hilly Hilly;

  double Arg[];
  const int Amount = inInputs.ToArray(Arg) >> 1;
  
  return(Hilly.CalcFunc(Arg, Amount));
}

#include <fxsaber\Optimization\Optimization_Addon.mqh> //https://www.mql5.com/ru/blogs/post/755815

设置。

自定义。

自定义

PSO Finished 5580 of 30000 planned passes: true
BestResult = 0.7742122055850458: X1 = -1.48, Y1 = 0.63, X2 = -1.48, Y2 = 0.63, X3 = 2.5100000000000002, Y3 = -3.0
Check = 0.7742122055850458: X1 = -1.48, Y1 = 0.63, X2 = -1.48, Y2 = 0.63, X3 = 2.5100000000000002, Y3 = -3.0

01: OPTIMIZATION_METHOD_AO_Micro_AIS
OPTIMIZATION_METHOD_AO_Micro_AIS
BestResult = 0.859449852020672: X1 = -1.51, Y1 = 0.5800000000000001, X2 = -1.4, Y2 = 0.5700000000000003, X3 = 0.52, Y3 = -0.48999999999999977
Check = 0.859449852020672: X1 = -1.51, Y1 = 0.5800000000000001, X2 = -1.4, Y2 = 0.5700000000000003, X3 = 0.52, Y3 = -0.48999999999999977

02: OPTIMIZATION_METHOD_AO_POES
OPTIMIZATION_METHOD_AO_POES
BestResult = 0.9647613369275468: X1 = -1.49, Y1 = 0.6499999999999999, X2 = -1.41, Y2 = 0.56, X3 = -1.54, Y3 = 0.6499999999999999
Check = 0.9647613369275468: X1 = -1.49, Y1 = 0.6499999999999999, X2 = -1.41, Y2 = 0.56, X3 = -1.54, Y3 = 0.6499999999999999

03: OPTIMIZATION_METHOD_AO_P_O_ES
OPTIMIZATION_METHOD_AO_P_O_ES
BestResult = 0.9858374371924213: X1 = -1.48, Y1 = 0.5800000000000001, X2 = -1.46, Y2 = 0.5800000000000001, X3 = -1.47, Y3 = 0.6499999999999999
Check = 0.9858374371924213: X1 = -1.48, Y1 = 0.5800000000000001, X2 = -1.46, Y2 = 0.5800000000000001, X3 = -1.47, Y3 = 0.6499999999999999

04: OPTIMIZATION_METHOD_AO_SC
OPTIMIZATION_METHOD_AO_SC
BestResult = 0.46044186528197245: X1 = -1.66, Y1 = 0.6499999999999999, X2 = 2.7300000000000004, Y2 = 1.9699999999999998, X3 = 2.24, Y3 = -1.3599999999999999
Check = 0.46044186528197245: X1 = -1.66, Y1 = 0.6499999999999999, X2 = 2.7300000000000004, Y2 = 1.9699999999999998, X3 = 2.24, Y3 = -1.3599999999999999

05: OPTIMIZATION_METHOD_AO_SIA
OPTIMIZATION_METHOD_AO_SIA
BestResult = 0.5396179505233242: X1 = -1.33, Y1 = 0.5100000000000002, X2 = -1.49, Y2 = 1.4900000000000002, X3 = -1.8, Y3 = 0.56
Check = 0.5396179505233242: X1 = -1.33, Y1 = 0.5100000000000002, X2 = -1.49, Y2 = 1.4900000000000002, X3 = -1.8, Y3 = 0.56

06: OPTIMIZATION_METHOD_AO_SA
OPTIMIZATION_METHOD_AO_SA
BestResult = 0.5321147995285683: X1 = 1.38, Y1 = -1.58, X2 = -1.38, Y2 = 0.45999999999999996, X3 = 2.46, Y3 = 1.2800000000000002
Check = 0.5321147995285683: X1 = 1.38, Y1 = -1.58, X2 = -1.38, Y2 = 0.45999999999999996, X3 = 2.46, Y3 = 1.2800000000000002

07: OPTIMIZATION_METHOD_AO_NMm
OPTIMIZATION_METHOD_AO_NMm
BestResult = 0.9920100032939798: X1 = -1.44, Y1 = 0.6099999999999999, X2 = -1.5, Y2 = 0.6000000000000001, X3 = -1.48, Y3 = 0.6200000000000001
Check = 0.9920100032939798: X1 = -1.44, Y1 = 0.6099999999999999, X2 = -1.5, Y2 = 0.6000000000000001, X3 = -1.48, Y3 = 0.6200000000000001

08: OPTIMIZATION_METHOD_AO_DE
OPTIMIZATION_METHOD_AO_DE
BestResult = 0.5455473633280449: X1 = -1.5, Y1 = 0.5700000000000003, X2 = -0.029999999999999805, Y2 = -0.8900000000000001, X3 = 1.42, Y3 = -1.3
Check = 0.5455473633280449: X1 = -1.5, Y1 = 0.5700000000000003, X2 = -0.029999999999999805, Y2 = -0.8900000000000001, X3 = 1.42, Y3 = -1.3

09: OPTIMIZATION_METHOD_AO_SDOm
OPTIMIZATION_METHOD_AO_SDOm
BestResult = 0.7851698884766712: X1 = -1.48, Y1 = 0.6099999999999999, X2 = -0.48999999999999977, Y2 = -2.57, X3 = -1.48, Y3 = 0.6099999999999999
Check = 0.7851698884766712: X1 = -1.48, Y1 = 0.6099999999999999, X2 = -0.48999999999999977, Y2 = -2.57, X3 = -1.48, Y3 = 0.6099999999999999

10: OPTIMIZATION_METHOD_AO_IWDm
OPTIMIZATION_METHOD_AO_IWDm
BestResult = 0.541122421125687: X1 = 1.6100000000000003, Y1 = 2.7300000000000004, X2 = -1.52, Y2 = 0.6299999999999999, X3 = -1.63, Y3 = 3.0
Check = 0.541122421125687: X1 = 1.6100000000000003, Y1 = 2.7300000000000004, X2 = -1.52, Y2 = 0.6299999999999999, X3 = -1.63, Y3 = 3.0

11: OPTIMIZATION_METHOD_AO_CSS
OPTIMIZATION_METHOD_AO_CSS
BestResult = 0.5193274099236366: X1 = -1.52, Y1 = 0.6699999999999999, X2 = 0.2400000000000002, Y2 = 2.24, X3 = -1.78, Y3 = -2.29
Check = 0.5193274099236366: X1 = -1.52, Y1 = 0.6699999999999999, X2 = 0.2400000000000002, Y2 = 2.24, X3 = -1.78, Y3 = -2.29

12: OPTIMIZATION_METHOD_AO_SDS
OPTIMIZATION_METHOD_AO_SDS
BestResult = 0.7382103272996998: X1 = -1.41, Y1 = 0.5899999999999999, X2 = 3.0, Y2 = 1.42, X3 = -1.43, Y3 = 0.6800000000000002
Check = 0.7382103272996998: X1 = -1.41, Y1 = 0.5899999999999999, X2 = 3.0, Y2 = 1.42, X3 = -1.43, Y3 = 0.6800000000000002

13: OPTIMIZATION_METHOD_AO_SDSm
OPTIMIZATION_METHOD_AO_SDSm
BestResult = 0.6404573711868022: X1 = -1.7, Y1 = 0.5999999999999996, X2 = -2.1, Y2 = -2.85, X3 = -1.55, Y3 = 0.5800000000000001
Check = 0.6404573711868022: X1 = -1.7, Y1 = 0.5999999999999996, X2 = -2.1, Y2 = -2.85, X3 = -1.55, Y3 = 0.5800000000000001

14: OPTIMIZATION_METHOD_AO_MEC
OPTIMIZATION_METHOD_AO_MEC
BestResult = 0.5746017381403192: X1 = -2.4299999999999997, Y1 = 1.4800000000000004, X2 = -1.47, Y2 = 0.6200000000000001, X3 = 0.52, Y3 = 2.4699999999999998
Check = 0.5746017381403192: X1 = -2.4299999999999997, Y1 = 1.4800000000000004, X2 = -1.47, Y2 = 0.6200000000000001, X3 = 0.52, Y3 = 2.4699999999999998

15: OPTIMIZATION_METHOD_AO_SFL
OPTIMIZATION_METHOD_AO_SFL
BestResult = 0.6012543161639043: X1 = -1.48, Y1 = 0.71, X2 = -1.48, Y2 = 0.9300000000000002, X3 = -1.18, Y3 = 1.4699999999999998
Check = 0.6012543161639043: X1 = -1.48, Y1 = 0.71, X2 = -1.48, Y2 = 0.9300000000000002, X3 = -1.18, Y3 = 1.4699999999999998

16: OPTIMIZATION_METHOD_AO_EM
OPTIMIZATION_METHOD_AO_EM
BestResult = 0.49859345948875217: X1 = -1.26, Y1 = 1.37, X2 = 2.1799999999999997, Y2 = -0.5299999999999998, X3 = -1.5, Y3 = 0.5
Check = 0.49859345948875217: X1 = -1.26, Y1 = 1.37, X2 = 2.1799999999999997, Y2 = -0.5299999999999998, X3 = -1.5, Y3 = 0.5

17: OPTIMIZATION_METHOD_AO_SSG
OPTIMIZATION_METHOD_AO_SSG
BestResult = 0.9248462969380026: X1 = -1.42, Y1 = 0.6499999999999999, X2 = -1.58, Y2 = 0.54, X3 = -1.42, Y3 = 0.5500000000000003
Check = 0.9248462969380026: X1 = -1.42, Y1 = 0.6499999999999999, X2 = -1.58, Y2 = 0.54, X3 = -1.42, Y3 = 0.5500000000000003

18: OPTIMIZATION_METHOD_AO_MA
OPTIMIZATION_METHOD_AO_MA
BestResult = 0.5319860043547983: X1 = 0.6000000000000001, Y1 = 1.7800000000000002, X2 = -1.42, Y2 = 0.5500000000000003, X3 = -1.48, Y3 = -2.59
Check = 0.5319860043547983: X1 = 0.6000000000000001, Y1 = 1.7800000000000002, X2 = -1.42, Y2 = 0.5500000000000003, X3 = -1.48, Y3 = -2.59

19: OPTIMIZATION_METHOD_AO_HS
OPTIMIZATION_METHOD_AO_HS

Error optimization!

20: OPTIMIZATION_METHOD_AO_GSA
OPTIMIZATION_METHOD_AO_GSA
BestResult = 0.571513952024667: X1 = 1.5700000000000003, Y1 = -1.48, X2 = -1.39, Y2 = 0.71, X3 = 1.5499999999999998, Y3 = -0.040000000000000036
Check = 0.571513952024667: X1 = 1.5700000000000003, Y1 = -1.48, X2 = -1.39, Y2 = 0.71, X3 = 1.5499999999999998, Y3 = -0.040000000000000036

21: OPTIMIZATION_METHOD_AO_GSA_Stars
OPTIMIZATION_METHOD_AO_GSA_Stars

Error optimization!

22: OPTIMIZATION_METHOD_AO_BFO
OPTIMIZATION_METHOD_AO_BFO
BestResult = 0.673690532910006: X1 = 1.5499999999999998, Y1 = 1.3899999999999997, X2 = 0.5, Y2 = -0.52, X3 = -1.47, Y3 = 0.6400000000000001
Check = 0.673690532910006: X1 = 1.5499999999999998, Y1 = 1.3899999999999997, X2 = 0.5, Y2 = -0.52, X3 = -1.47, Y3 = 0.6400000000000001

23: OPTIMIZATION_METHOD_AO_IWO
OPTIMIZATION_METHOD_AO_IWO
BestResult = 0.5624806395733428: X1 = 1.4900000000000002, Y1 = 1.2999999999999998, X2 = 0.43999999999999995, Y2 = -0.48999999999999977, X3 = -1.42, Y3 = 0.6400000000000001
Check = 0.6266957817897628: X1 = 1.4900000000000002, Y1 = 1.2999999999999998, X2 = 0.43999999999999995, Y2 = -0.48999999999999977, X3 = -1.42, Y3 = 0.6400000000000001

24: OPTIMIZATION_METHOD_AO_BA
OPTIMIZATION_METHOD_AO_BA
BestResult = 0.5690853945437194: X1 = 0.48, Y1 = -1.54, X2 = -1.48, Y2 = 0.6200000000000001, X3 = -0.44999999999999973, Y3 = 2.5200000000000005
Check = 0.5690853945437194: X1 = 0.48, Y1 = -1.54, X2 = -1.48, Y2 = 0.6200000000000001, X3 = -0.44999999999999973, Y3 = 2.5200000000000005

25: OPTIMIZATION_METHOD_AO_FAm
OPTIMIZATION_METHOD_AO_FAm
BestResult = 0.5778309203162327: X1 = -1.47, Y1 = 0.6200000000000001, X2 = -1.47, Y2 = 2.5600000000000005, X3 = -2.54, Y3 = 2.3600000000000003
Check = 0.5778309203162327: X1 = -1.47, Y1 = 0.6200000000000001, X2 = -1.47, Y2 = 2.5600000000000005, X3 = -2.54, Y3 = 2.3600000000000003

26: OPTIMIZATION_METHOD_AO_FSS
OPTIMIZATION_METHOD_AO_FSS
BestResult = 0.4978927704570393: X1 = 0.010000000000000231, Y1 = 0.3200000000000003, X2 = -2.08, Y2 = -1.8, X3 = -1.48, Y3 = 0.6000000000000001
Check = 0.4978927704570393: X1 = 0.010000000000000231, Y1 = 0.3200000000000003, X2 = -2.08, Y2 = -1.8, X3 = -1.48, Y3 = 0.6000000000000001

27: OPTIMIZATION_METHOD_AO_COAm
OPTIMIZATION_METHOD_AO_COAm
BestResult = 0.6778174074019874: X1 = -2.2800000000000002, Y1 = 0.14000000000000012, X2 = -1.3499999999999999, Y2 = 0.6600000000000001, X3 = -1.55, Y3 = 0.54
Check = 0.6778174074019874: X1 = -2.2800000000000002, Y1 = 0.14000000000000012, X2 = -1.3499999999999999, Y2 = 0.6600000000000001, X3 = -1.55, Y3 = 0.54

28: OPTIMIZATION_METHOD_AO_GWO
OPTIMIZATION_METHOD_AO_GWO
BestResult = 0.542753660101771: X1 = -0.1299999999999999, Y1 = 0.14000000000000012, X2 = 1.5700000000000003, Y2 = -1.68, X3 = -1.5, Y3 = 0.7200000000000002
Check = 0.542753660101771: X1 = -0.1299999999999999, Y1 = 0.14000000000000012, X2 = 1.5700000000000003, Y2 = -1.68, X3 = -1.5, Y3 = 0.7200000000000002

29: OPTIMIZATION_METHOD_AO_ABC
OPTIMIZATION_METHOD_AO_ABC
BestResult = 0.49786755065740795: X1 = -0.040000000000000036, Y1 = 0.29000000000000004, X2 = -2.0300000000000002, Y2 = -1.76, X3 = -1.49, Y3 = 0.6099999999999999
Check = 0.49786755065740795: X1 = -0.040000000000000036, Y1 = 0.29000000000000004, X2 = -2.0300000000000002, Y2 = -1.76, X3 = -1.49, Y3 = 0.6099999999999999

30: OPTIMIZATION_METHOD_AO_ACOm
OPTIMIZATION_METHOD_AO_ACOm
BestResult = 0.8716708506315909: X1 = -1.49, Y1 = 0.6600000000000001, X2 = -1.51, Y2 = 0.6000000000000001, X3 = 0.54, Y3 = -0.48999999999999977
Check = 0.8716708506315909: X1 = -1.49, Y1 = 0.6600000000000001, X2 = -1.51, Y2 = 0.6000000000000001, X3 = 0.54, Y3 = -0.48999999999999977

31: OPTIMIZATION_METHOD_AO_PSO
OPTIMIZATION_METHOD_AO_PSO
BestResult = 0.5508486039662627: X1 = 1.4100000000000001, Y1 = 1.4400000000000004, X2 = -1.49, Y2 = 0.71, X3 = 2.38, Y3 = 1.5
Check = 0.5508486039662627: X1 = 1.4100000000000001, Y1 = 1.4400000000000004, X2 = -1.49, Y2 = 0.71, X3 = 2.38, Y3 = 1.5

32: OPTIMIZATION_METHOD_AO_RND
OPTIMIZATION_METHOD_AO_RND
BestResult = 0.5036403607427178: X1 = -2.96, Y1 = -0.54, X2 = 0.8799999999999999, Y2 = -1.64, X3 = -1.58, Y3 = 0.6200000000000001
Check = 0.5036403607427178: X1 = -2.96, Y1 = -0.54, X2 = 0.8799999999999999, Y2 = -1.64, X3 = -1.58, Y3 = 0.6200000000000001

Andrey Dik 2024.01.21 06:46 #29

fxsaber #:

根据这种方法，可以进行希利功能测试。

在这里，情况发生了变化。

PSO 和 IWDm 在结果列表中名列前茅，显示的数值处于极限范围，这并不是很好。

OPTIMIZATION_METHOD_AO_GSA_Stars

GSA_Stars 只是一个玩具，用于身体运动的视觉模拟，可以去掉。

而 HS 出于某种原因，是非常有趣的算法。

fxsaber 2024.01.21 11:12 #30

Andrey Dik #:

由于某种原因，HS Erorit 的算法非常有趣。

在 Optimisation.mqh 中就有相关介绍。

// 优化_C_AO_HS
#define  MACROS_OPTIMIZATION_INIT , 0.9, 0.1, 0.2, epochCount
  // 不能在不修改源代码的情况下进行调整。
  // 名称匹配：C_AO_HS::h[] и S_Harmony::h.

如果您更改源代码（使名称不匹配），我可以对其进行改编。

文章 "种群优化算法：微人工免疫系统（Micro-AIS）" - 页 3