Discussing the article: "Population optimization algorithms: Micro Artificial immune system (Micro-AIS)"
| № | AO | Description | Hilly | Hilly final | Forest | Forest final | Megacity (discrete) | Megacity final | Final result | % of MAX | ||||||
| 10 p (5 F) | 50 p (25 F) | 1000 p (500 F) | 10 p (5 F) | 50 p (25 F) | 1000 p (500 F) | 10 p (5 F) | 50 p (25 F) | 1000 p (500 F) | ||||||||
| 1 | (P+O)ES | (P+O) evolution strategies | 0,99934 | 0,91895 | 0,56297 | 2,48127 | 1,00000 | 0,93522 | 0,39179 | 2,32701 | 0,83167 | 0,64433 | 0,21155 | 1,68755 | 6,496 | 72,18 |
| 2 | SDSm | stochastic diffusion search M | 0,93066 | 0,85445 | 0,39476 | 2,17988 | 0,99983 | 0,89244 | 0,19619 | 2,08846 | 0,72333 | 0,61100 | 0,10670 | 1,44103 | 5,709 | 63,44 |
Please link to the relevant articles in this table.
Neural networks, algorithms, optimisation, learning - it's all great.
What algorithm can be used to read a message? 
In systems analysis, what is important are the elements of the system and the functional relationships between them, united by the meaning (purpose) of the system's existence.
How justified is it to apply stochastic analysis where we should be looking to apply functional analysis.
The question may be sideways, but in the direction of understanding the fundamental basis of the process.
Forum on trading, automated trading systems and testing trading strategies
fxsaber, 2024.01.19 18:16
Example of library application for custom optimisation algorithms. 
Is it difficult to optimise a function of a species?
double FF( const double &Arg[] ) { double Res = 1; for (uint i = ArraySize(Arg); (bool)i--;) Res *= MathSin(Arg[i]); return(Res); }The best values should be close to one. But can the algorithms in this series of articles approach this maximum?
fxsaber #:
For a long time I tried to drag algorithms of this series into the optimiser (I wanted to parallelise them), but it glitches epically - https://www.mql5.com/en/forum/454524/page2#comment_50233782.
MT5 forward testing bugs.
- 2023.10.20
- www.mql5.com
This post is about a some weirdly related (I think) bugs that happen when forward testing optimized results. Please read through it all...
Stanislav Korotky #:
For a long time I tried to drag algorithms of this series into the optimiser (I wanted to parallelise them), but it glitches epically - https://www.mql5.com/en/forum/454524/page2#comment_50233782.
Optimization - несколько алгоритмов оптимизации в одном месте.
- www.mql5.com
Получилось собрать в одном месте сразу несколько алгоритмов оптимизации и создать простой механизм их использования. Механизм. Помещаем советник в Тестер и используем GUI вкладки Inputs , чтобы
Forum on trading, automated trading systems and testing trading strategies
fxsaber, 2024.01.19 22:30
Is it difficult to optimise a function of a species?double FF( const double &Arg[] ) { double Res = 1; for (uint i = ArraySize(Arg); (bool)i--;) Res *= MathSin(Arg[i]); return(Res); }The best values should be close to one. But can the algorithms in this series of articles approach this maximum?
Tested.
#define dInput01 X1 #define dInput02 X2 #define dInput03 X3 #define dInput04 X4 #define dInput05 X5 #define dInput06 X6 #define dInput07 X7 #include <fxsaber\Input_Struct\Input_Struct.mqh> // https://www.mql5.com/ru/code/47932 INPUT_STRUCT inInputs; MACROS_INPUT(double, X1, 0); MACROS_INPUT(double, X2, 0); MACROS_INPUT(double, X3, 0); MACROS_INPUT(double, X4, 0); MACROS_INPUT(double, X5, 0); MACROS_INPUT(double, X6, 0); MACROS_INPUT(double, X7, 0); // ФФ для штатного Оптимизатора - пример. double OnTester() { return(MathSin(inInputs.X1) * MathSin(inInputs.X2) * MathSin(inInputs.X3) * MathSin(inInputs.X4) * MathSin(inInputs.X5) * MathSin(inInputs.X6) * MathSin(inInputs.X7)); } #include <fxsaber\Optimization\Optimization_Addon.mqh> // https://www.mql5.com/ru/blogs/post/755815
Inputs.
Custom Optimiser.
Custom.
PSO Finished 15835 of 35000 planned passes: true BestResult = 0.9884554736115849: X1 = 99.0, X2 = 99.0, X3 = 11.0, X4 = 77.0, X5 = 14.0, X6 = 11.0, X7 = 33.0 Check = 0.9884554736115849: X1 = 99.0, X2 = 99.0, X3 = 11.0, X4 = 77.0, X5 = 14.0, X6 = 11.0, X7 = 33.0 01: OPTIMIZATION_METHOD_AO_Micro_AIS OPTIMIZATION_METHOD_AO_Micro_AIS BestResult = 0.6914924547679845: X1 = 17.0, X2 = 89.0, X3 = 61.0, X4 = 33.0, X5 = 71.0, X6 = 64.0, X7 = 8.0 Check = 0.6914924547679845: X1 = 17.0, X2 = 89.0, X3 = 61.0, X4 = 33.0, X5 = 71.0, X6 = 64.0, X7 = 8.0 02: OPTIMIZATION_METHOD_AO_POES OPTIMIZATION_METHOD_AO_POES BestResult = 0.9268682527605293: X1 = 55.0, X2 = 80.0, X3 = 27.0, X4 = 99.0, X5 = 8.0, X6 = 52.0, X7 = 11.0 Check = 0.9268682527605293: X1 = 55.0, X2 = 80.0, X3 = 27.0, X4 = 99.0, X5 = 8.0, X6 = 52.0, X7 = 11.0 03: OPTIMIZATION_METHOD_AO_P_O_ES OPTIMIZATION_METHOD_AO_P_O_ES BestResult = 0.7717845794829589: X1 = 11.0, X2 = 49.0, X3 = 74.0, X4 = 30.0, X5 = 11.0, X6 = 77.0, X7 = 43.0 Check = 0.7717845794829589: X1 = 11.0, X2 = 49.0, X3 = 74.0, X4 = 30.0, X5 = 11.0, X6 = 77.0, X7 = 43.0 04: OPTIMIZATION_METHOD_AO_SC OPTIMIZATION_METHOD_AO_SC BestResult = 0.5703083565001157: X1 = 4.0, X2 = 39.0, X3 = 20.0, X4 = 93.0, X5 = 8.0, X6 = 20.0, X7 = 33.0 Check = 0.5703083565001157: X1 = 4.0, X2 = 39.0, X3 = 20.0, X4 = 93.0, X5 = 8.0, X6 = 20.0, X7 = 33.0 05: OPTIMIZATION_METHOD_AO_SIA OPTIMIZATION_METHOD_AO_SIA BestResult = 0.3770511069126069: X1 = 30.0, X2 = 55.0, X3 = 49.0, X4 = 77.0, X5 = 100.0, X6 = 65.0, X7 = 27.0 Check = 0.3770511069126069: X1 = 30.0, X2 = 55.0, X3 = 49.0, X4 = 77.0, X5 = 100.0, X6 = 65.0, X7 = 27.0 06: OPTIMIZATION_METHOD_AO_SA OPTIMIZATION_METHOD_AO_SA BestResult = 0.4195625904721657: X1 = 58.0, X2 = 77.0, X3 = 27.0, X4 = 40.0, X5 = 70.0, X6 = 14.0, X7 = 70.0 Check = 0.4195625904721657: X1 = 58.0, X2 = 77.0, X3 = 27.0, X4 = 40.0, X5 = 70.0, X6 = 14.0, X7 = 70.0 07: OPTIMIZATION_METHOD_AO_NMm OPTIMIZATION_METHOD_AO_NMm BestResult = 0.8314291991406518: X1 = 30.0, X2 = 46.0, X3 = 99.0, X4 = 11.0, X5 = 96.0, X6 = 39.0, X7 = 74.0 Check = 0.8314291991406518: X1 = 30.0, X2 = 46.0, X3 = 99.0, X4 = 11.0, X5 = 96.0, X6 = 39.0, X7 = 74.0 08: OPTIMIZATION_METHOD_AO_DE OPTIMIZATION_METHOD_AO_DE BestResult = 0.514763435265798: X1 = 33.0, X2 = 39.0, X3 = 49.0, X4 = 20.0, X5 = 73.0, X6 = 20.0, X7 = 58.0 Check = 0.514763435265798: X1 = 33.0, X2 = 39.0, X3 = 49.0, X4 = 20.0, X5 = 73.0, X6 = 20.0, X7 = 58.0 09: OPTIMIZATION_METHOD_AO_SDOm OPTIMIZATION_METHOD_AO_SDOm BestResult = 0.6248310950237546: X1 = 55.0, X2 = 61.0, X3 = 20.0, X4 = 71.0, X5 = 26.0, X6 = 74.0, X7 = 36.0 Check = 0.6248310950237546: X1 = 55.0, X2 = 61.0, X3 = 20.0, X4 = 71.0, X5 = 26.0, X6 = 74.0, X7 = 36.0 10: OPTIMIZATION_METHOD_AO_IWDm OPTIMIZATION_METHOD_AO_IWDm BestResult = 0.6582185170915256: X1 = 33.0, X2 = 24.0, X3 = 61.0, X4 = 55.0, X5 = 46.0, X6 = 36.0, X7 = 1.0 Check = 0.6582185170915256: X1 = 33.0, X2 = 24.0, X3 = 61.0, X4 = 55.0, X5 = 46.0, X6 = 36.0, X7 = 1.0 11: OPTIMIZATION_METHOD_AO_CSS OPTIMIZATION_METHOD_AO_CSS BestResult = 0.17125241139972677: X1 = 11.0, X2 = 5.0, X3 = 11.0, X4 = 37.0, X5 = 56.0, X6 = 65.0, X7 = 37.0 Check = 0.17125241139972677: X1 = 11.0, X2 = 5.0, X3 = 11.0, X4 = 37.0, X5 = 56.0, X6 = 65.0, X7 = 37.0 12: OPTIMIZATION_METHOD_AO_SDS OPTIMIZATION_METHOD_AO_SDS BestResult = 0.7015125972513457: X1 = 17.0, X2 = 46.0, X3 = 27.0, X4 = 39.0, X5 = 77.0, X6 = 71.0, X7 = 86.0 Check = 0.7015125972513457: X1 = 17.0, X2 = 46.0, X3 = 27.0, X4 = 39.0, X5 = 77.0, X6 = 71.0, X7 = 86.0 13: OPTIMIZATION_METHOD_AO_SDSm OPTIMIZATION_METHOD_AO_SDSm BestResult = 0.8318883232825393: X1 = 77.0, X2 = 14.0, X3 = 14.0, X4 = 30.0, X5 = 80.0, X6 = 49.0, X7 = 24.0 Check = 0.8318883232825393: X1 = 77.0, X2 = 14.0, X3 = 14.0, X4 = 30.0, X5 = 80.0, X6 = 49.0, X7 = 24.0 14: OPTIMIZATION_METHOD_AO_MEC OPTIMIZATION_METHOD_AO_MEC BestResult = 0.821421124921697: X1 = 99.0, X2 = 58.0, X3 = 90.0, X4 = 27.0, X5 = 14.0, X6 = 80.0, X7 = 96.0 Check = 0.821421124921697: X1 = 99.0, X2 = 58.0, X3 = 90.0, X4 = 27.0, X5 = 14.0, X6 = 80.0, X7 = 96.0 15: OPTIMIZATION_METHOD_AO_SFL OPTIMIZATION_METHOD_AO_SFL BestResult = 0.7123520662251704: X1 = 49.0, X2 = 52.0, X3 = 80.0, X4 = 93.0, X5 = 52.0, X6 = 87.0, X7 = 14.0 Check = 0.7123520662251704: X1 = 49.0, X2 = 52.0, X3 = 80.0, X4 = 93.0, X5 = 52.0, X6 = 87.0, X7 = 14.0 16: OPTIMIZATION_METHOD_AO_EM OPTIMIZATION_METHOD_AO_EM BestResult = 0.4739892519704631: X1 = 39.0, X2 = 96.0, X3 = 49.0, X4 = 54.0, X5 = 93.0, X6 = 8.0, X7 = 11.0 Check = 0.4739892519704631: X1 = 39.0, X2 = 96.0, X3 = 49.0, X4 = 54.0, X5 = 93.0, X6 = 8.0, X7 = 11.0 17: OPTIMIZATION_METHOD_AO_SSG OPTIMIZATION_METHOD_AO_SSG BestResult = 0.7570642423726676: X1 = 5.0, X2 = 49.0, X3 = 30.0, X4 = 96.0, X5 = 14.0, X6 = 55.0, X7 = 89.0 Check = 0.7570642423726676: X1 = 5.0, X2 = 49.0, X3 = 30.0, X4 = 96.0, X5 = 14.0, X6 = 55.0, X7 = 89.0 18: OPTIMIZATION_METHOD_AO_MA OPTIMIZATION_METHOD_AO_MA BestResult = 0.7831093525101701: X1 = 93.0, X2 = 36.0, X3 = 17.0, X4 = 58.0, X5 = 42.0, X6 = 61.0, X7 = 74.0 Check = 0.7831093525101701: X1 = 93.0, X2 = 36.0, X3 = 17.0, X4 = 58.0, X5 = 42.0, X6 = 61.0, X7 = 74.0 19: OPTIMIZATION_METHOD_AO_HS OPTIMIZATION_METHOD_AO_HS Error optimization! 20: OPTIMIZATION_METHOD_AO_GSA OPTIMIZATION_METHOD_AO_GSA BestResult = 0.020184193323560605: X1 = 9.0, X2 = 27.0, X3 = 54.0, X4 = 6.0, X5 = 77.0, X6 = 75.0, X7 = 23.0 Check = 0.020184193323560605: X1 = 9.0, X2 = 27.0, X3 = 54.0, X4 = 6.0, X5 = 77.0, X6 = 75.0, X7 = 23.0 21: OPTIMIZATION_METHOD_AO_GSA_Stars OPTIMIZATION_METHOD_AO_GSA_Stars Error optimization! 22: OPTIMIZATION_METHOD_AO_BFO OPTIMIZATION_METHOD_AO_BFO BestResult = 0.7322059190279094: X1 = 20.0, X2 = 11.0, X3 = 52.0, X4 = 49.0, X5 = 89.0, X6 = 36.0, X7 = 99.0 Check = 0.7322059190279094: X1 = 20.0, X2 = 11.0, X3 = 52.0, X4 = 49.0, X5 = 89.0, X6 = 36.0, X7 = 99.0 23: OPTIMIZATION_METHOD_AO_IWO OPTIMIZATION_METHOD_AO_IWO BestResult = 0.7392111937754324: X1 = 20.0, X2 = 11.0, X3 = 52.0, X4 = 49.0, X5 = 89.0, X6 = 37.0, X7 = 100.0 Check = 0.24076952243473274: X1 = 20.0, X2 = 11.0, X3 = 52.0, X4 = 49.0, X5 = 89.0, X6 = 37.0, X7 = 100.0 24: OPTIMIZATION_METHOD_AO_BA OPTIMIZATION_METHOD_AO_BA BestResult = 0.35033516894855804: X1 = 98.0, X2 = 49.0, X3 = 92.0, X4 = 77.0, X5 = 96.0, X6 = 99.0, X7 = 21.0 Check = 0.35033516894855804: X1 = 98.0, X2 = 49.0, X3 = 92.0, X4 = 77.0, X5 = 96.0, X6 = 99.0, X7 = 21.0 25: OPTIMIZATION_METHOD_AO_FAm OPTIMIZATION_METHOD_AO_FAm BestResult = 0.8628261244286874: X1 = 61.0, X2 = 33.0, X3 = 93.0, X4 = 55.0, X5 = 30.0, X6 = 49.0, X7 = 55.0 Check = 0.8628261244286874: X1 = 61.0, X2 = 33.0, X3 = 93.0, X4 = 55.0, X5 = 30.0, X6 = 49.0, X7 = 55.0 26: OPTIMIZATION_METHOD_AO_FSS OPTIMIZATION_METHOD_AO_FSS BestResult = 0.6586267117021989: X1 = 90.0, X2 = 17.0, X3 = 30.0, X4 = 11.0, X5 = 11.0, X6 = 89.0, X7 = 46.0 Check = 0.6586267117021989: X1 = 90.0, X2 = 17.0, X3 = 30.0, X4 = 11.0, X5 = 11.0, X6 = 89.0, X7 = 46.0 27: OPTIMIZATION_METHOD_AO_COAm OPTIMIZATION_METHOD_AO_COAm BestResult = 0.751387775021677: X1 = 33.0, X2 = 74.0, X3 = 89.0, X4 = 52.0, X5 = 2.0, X6 = 8.0, X7 = 99.0 Check = 0.751387775021677: X1 = 33.0, X2 = 74.0, X3 = 89.0, X4 = 52.0, X5 = 2.0, X6 = 8.0, X7 = 99.0 28: OPTIMIZATION_METHOD_AO_GWO OPTIMIZATION_METHOD_AO_GWO BestResult = 0.7905125996746682: X1 = 64.0, X2 = 24.0, X3 = 58.0, X4 = 11.0, X5 = 39.0, X6 = 36.0, X7 = 55.0 Check = 0.7905125996746682: X1 = 64.0, X2 = 24.0, X3 = 58.0, X4 = 11.0, X5 = 39.0, X6 = 36.0, X7 = 55.0 29: OPTIMIZATION_METHOD_AO_ABC OPTIMIZATION_METHOD_AO_ABC BestResult = 0.2279828722733523: X1 = 37.0, X2 = 49.0, X3 = 45.0, X4 = 96.0, X5 = 86.0, X6 = 54.0, X7 = 89.0 Check = 0.2279828722733523: X1 = 37.0, X2 = 49.0, X3 = 45.0, X4 = 96.0, X5 = 86.0, X6 = 54.0, X7 = 89.0 30: OPTIMIZATION_METHOD_AO_ACOm OPTIMIZATION_METHOD_AO_ACOm BestResult = 0.7283588705105443: X1 = 58.0, X2 = 36.0, X3 = 46.0, X4 = 58.0, X5 = 77.0, X6 = 42.0, X7 = 46.0 Check = 0.7283588705105443: X1 = 58.0, X2 = 36.0, X3 = 46.0, X4 = 58.0, X5 = 77.0, X6 = 42.0, X7 = 46.0 31: OPTIMIZATION_METHOD_AO_PSO OPTIMIZATION_METHOD_AO_PSO BestResult = 0.5892210470192797: X1 = 52.0, X2 = 52.0, X3 = 68.0, X4 = 62.0, X5 = 86.0, X6 = 77.0, X7 = 30.0 Check = 0.5892210470192797: X1 = 52.0, X2 = 52.0, X3 = 68.0, X4 = 62.0, X5 = 86.0, X6 = 77.0, X7 = 30.0 32: OPTIMIZATION_METHOD_AO_RND OPTIMIZATION_METHOD_AO_RND BestResult = 0.6663782757838177: X1 = 4.896755719304697, X2 = 61.0, X3 = 23.0, X4 = 8.0, X5 = 52.0, X6 = 67.0, X7 = 58.0 Check = 0.6663782757838177: X1 = 4.896755719304697, X2 = 61.0, X3 = 23.0, X4 = 8.0, X5 = 52.0, X6 = 67.0, X7 = 58.0
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Check out the new article: Population optimization algorithms: Micro Artificial immune system (Micro-AIS).
The article considers an optimization method based on the principles of the body's immune system - Micro Artificial Immune System (Micro-AIS) - a modification of AIS. Micro-AIS uses a simpler model of the immune system and simple immune information processing operations. The article also discusses the advantages and disadvantages of Micro-AIS compared to conventional AIS.
The AIS algorithm models these processes using the concepts of antigens (inputs), antibodies (solutions) and killer cells (optimization processes) to optimally solve the problem. Antigens represent the inputs that need to be optimized. Antibodies represent potential solutions to the problem. Killer cells are optimization processes that search for the best solutions to an optimization problem.
Artificial Immune System (AIS) optimization method was proposed in the 1990s. Early research on this method dates back to the mid-1980s, with significant contributions by Farmer, Packard, Perelson (1986) and Bersini and Varela (1990).
Since then, the AIS method has continued to develop and be the subject of ongoing research in the scientific community. Many variations and modifications of this method have been proposed, as well as its application to various optimization and learning problems. The body's immune system also plays an important role in protecting against external influences, such as infections and tumors. It has the ability to recognize and detect anomalies and attack hostile agents, while maintaining the ability to distinguish and store information about them for future use.
Micro-AIS (Micro-Immune Algorithm) is a modification of the immune system (AIS) algorithm that was developed to solve optimization problems. It differs from conventional AIS in that it uses a simpler model of the immune system and simpler immune information processing operations.
Author: Andrey Dik