Русский Deutsch 日本語
preview
Population optimization algorithms: Grey Wolf Optimizer (GWO)

Population optimization algorithms: Grey Wolf Optimizer (GWO)

MetaTrader 5Examples | 20 January 2023, 15:18
3 134 1
Andrey Dik
Andrey Dik

Contents

1. Introduction
2. Algorithm description
3. Test functions
4. Test results


1. Introduction

The gray wolf algorithm is a metaheuristic stochastic swarm intelligence algorithm developed in 2014. Its idea is based on the gray wolf pack hunting model. There are four types of wolves: alpha, beta, delta and omega. Alpha has the most "weight" in decision making and managing the pack. Next come the beta and the delta, which obey the alpha and have power over the rest of the wolves. The omega wolf always obeys the rest of the dominant wolves.

In the wolf hierarchy mathematical model, the alpha-α-wolf is considered the dominant wolf in the pack, and his orders should be carried out by the members of the pack. Beta-β-subordinate wolves assist the alpha in decision making and are considered the best candidates for the role of alpha. Delta wolves δ should obey alpha and beta, but they dominate omega. Omega ω wolves are considered the scapegoats of the pack and the least important individuals. They are only allowed to eat at the end. Alpha is considered the most favorable solution.

The second and third best solutions are beta and delta, respectively. The rest of the solutions are considered omega. It is assumed that the fittest wolves (alpha, beta and delta), that is, those closest to the prey, will be approached by the rest of the wolves. After each approach, it is determined who is alpha, beta and delta at this stage, and then the wolves are rearranged again. The formation takes place until the wolves gather in a pack, which will be the optimal direction for an attack with a minimum distance.

During the algorithm, 3 main stages are performed, in which the wolves search for prey, surround and attack it. The search reveals alpha, beta and delta - the wolves that are closest to the prey. The rest, obeying the dominant ones, may begin to surround the prey or continue to randomly move in search of the best option.


2. Algorithm description

The hierarchy in the pack is schematically displayed in Figure 1.  Alpha plays a dominant role.

dominance

Fig. 1. Social hierarchy in a pack of wolves




Mathematical model and algorithm
Social hierarchy:

  • The best solution in the form of an alpha wolf (α).
  • The second best solution as a beta wolf (β).
  • The third best solution as a delta wolf (δ).
  • Other possible solutions as omega volves (ω).

Encircling prey: when there are already the best alpha, beta and delta solutions, the further actions depend on the omega.



stages

Fig 2. Hunting stages: search, encircling, attack.


All iterations of the algorithm are represented by three stages: search, encircling and hunting. The canonical version of the algorithm features the а calculated ratio introduced to improve the convergence of the algorithm. The ratio decreases to zero at each iteration. As long as the ratio exceeds 1, the initialization of the wolves is in progress. At this stage, the position of the prey is completely unknown, so the wolves should be distributed randomly.

After the "search" stage, the value of the fitness function is determined, and only after that it is possible to proceed to the "encircling" stage. At this stage, the a ratio is greater than 1. This means that alpha, beta and delta are moving away from their previous positions, thus allowing the position of the estimated prey to be refined. When the а ratio becomes equal to 1, the "attack" stage starts, while the ratio tends to 0 before the end of the iterations. This causes the wolves to approach the prey, suggesting that the best position has already been found. Although, if at this stage one of the wolves finds a better solution, then the position of the prey and the hierarchy of the wolves will be updated, but the ratio will still tend to 0. The process of changing a is represented by a non-linear function. The stages are schematically shown in Figure 2.

The behavior of omega wolves is unchanged throughout all epochs and consists in following the geometric center between the positions of the currently dominant individuals. In Figure 3, alpha, beta and delta deviate from their previous position in a random direction with a radius that is given by the coefficients, and omegas move to the center between them but with some degree of probability deviating from it within the radius. The radii determine the а ratio, which, as we remember, changes causing the radii to decrease proportionally.




surrounding

Fig. 3. Diagram of the omega movement in relation to alpha, beta and delta


The pseudocode of the GWO algorithm is as follows:

1) Randomly initialize the gray wolf population.
2) Calculate the fitness of each member of the population.
3) Pack leaders:
-α = member with the best fitness value
-β = second best member (in terms of fitness)
-δ = third best member (in terms of fitness value)
Update the position of all omega wolves according to the equations depending on α, β, δ
4) Calculate the fitness of each member of the population.
5) repeat step 3.

Let's move on to the algorithm code. The only addition I made to the original version is the ability to set the number of leading wolves in the pack. Now you can set any number of leaders, up to the entire pack. This may be useful for specific tasks.

We start, as usual, with the elementary unit of the algorithm - the wolf, which is a solution to the problem. This is a structure that includes an array of coordinates and a prey value (fitness functions). The structure is the same for leaders and minor members of the pack. This simplifies the algorithm and allows us to use the same structures in the loop operations. Moreover, during all iterations, the roles of the wolves change many times. The roles are uniquely determined by the position in the array after sorting. The leaders are at the beginning of the array.

//——————————————————————————————————————————————————————————————————————————————
struct S_Wolf
{
  double c []; //coordinates
  double p;    //prey
};
//——————————————————————————————————————————————————————————————————————————————

The wolf pack is represented by a compact and understandable class. Here we declare the ranges and step of the parameters to be optimized, the best production position, the best solution value and auxiliary functions.

//——————————————————————————————————————————————————————————————————————————————
class C_AO_GWO //wolfpack
{
  //============================================================================
  public: double rangeMax  []; //maximum search range
  public: double rangeMin  []; //manimum search range
  public: double rangeStep []; //step search
  public: S_Wolf wolves    []; //wolves of the pack
  public: double cB        []; //best prey coordinates
  public: double pB;           //best prey

  public: void InitPack (const int    coordinatesP,   //number of opt. parameters
                         const int    wolvesNumberP,  //wolves number
                         const int    alphaNumberP,   //alpha beta delta number
                         const int    epochCountP);   //epochs number

  public: void TasksForWolves      (int epochNow);
  public: void RevisionAlphaStatus ();


  //============================================================================
  private: void   ReturnToRange (S_Wolf &wolf);
  private: void   SortingWolves ();
  private: double SeInDiSp      (double In, double InMin, double InMax, double Step);
  private: double RNDfromCI     (double Min, double Max);

  private: int    coordinates;     //coordinates number
  private: int    wolvesNumber;    //the number of all wolves
  private: int    alphaNumber;     //Alpha beta delta number of all wolves
  private: int    epochCount;

  private: S_Wolf wolvesT    [];   //temporary, for sorting
  private: int    ind        [];   //array for indexes when sorting
  private: double val        [];   //array for sorting

  private: bool   searching;       //searching flag
};
//——————————————————————————————————————————————————————————————————————————————

Traditionally, the class declaration is followed by the initialization. Here we reset to the minimum 'double' value of the wolves' fitness and distribute the size of the arrays.

//——————————————————————————————————————————————————————————————————————————————
void C_AO_GWO::InitPack (const int    coordinatesP,   //number of opt. parameters
                         const int    wolvesNumberP,  //wolves number
                         const int    alphaNumberP,   //alpha beta delta number
                         const int    epochCountP)    //epochs number
{
  MathSrand (GetTickCount ());
  searching = false;
  pB        = -DBL_MAX;

  coordinates  = coordinatesP;
  wolvesNumber = wolvesNumberP;
  alphaNumber  = alphaNumberP;
  epochCount   = epochCountP;

  ArrayResize (rangeMax,  coordinates);
  ArrayResize (rangeMin,  coordinates);
  ArrayResize (rangeStep, coordinates);
  ArrayResize (cB,        coordinates);

  ArrayResize (ind, wolvesNumber);
  ArrayResize (val, wolvesNumber);

  ArrayResize (wolves,  wolvesNumber);
  ArrayResize (wolvesT, wolvesNumber);

  for (int i = 0; i < wolvesNumber; i++)
  {
    ArrayResize (wolves  [i].c, coordinates);
    ArrayResize (wolvesT [i].c, coordinates);
    wolves  [i].p = -DBL_MAX;
    wolvesT [i].p = -DBL_MAX;
  }
}
//——————————————————————————————————————————————————————————————————————————————

The first public method called at each iteration is the most difficult to understand and the most voluminous. Here is the main logic of the algorithm. In fact, the performance of the algorithm is provided by a probabilistic mechanism strictly described by equations. Let's consider this method step by step. On the first iteration, when the position of the intended prey is unknown, after checking the flag, we send the wolves in a random direction simply by generating values from the max and min of the optimized parameters.

//----------------------------------------------------------------------------
//space has not been explored yet, then send the wolf in a random direction
if (!searching)
{
  for (int w = 0; w < wolvesNumber; w++)
  {
    for (int c = 0; c < coordinates; c++)
    {
      wolves [w].c [c] = RNDfromCI (rangeMin [c], rangeMax [c]);
      wolves [w].c [c] = SeInDiSp  (wolves [w].c [c], rangeMin [c], rangeMax [c], rangeStep [c]);
    }
  }
   
  searching = true;
  return;
}

In the canonical version of the algorithm description, there are equations that operate vectors. However, they are much clearer in the form of code. The calculation of omega wolves goes is performed prior to the calculation of alpha, beta and delta wolves because we need to use the previous leader values.

The main component that provides three stages of hunting (search, encirclement and attack) is the a ratio. It represents a non-linear dependence on the current iteration and the total number of iterations, and tends to 0.
The next components of the equation are Ai and Сi:
  • Ai = 2.0 * a * r1 - a;
  • Ci = 2.0 * r2;
where r1 and r2 are random numbers in the range [0.0;1.0].
In the expression
Xn += wolves [abd].c [c] - Ai * (Ci * wolves [abd].c [c] - wolves [w].c [c]);
the coordinates of the wolves are adjusted according to the average values of the leader wolves. Since any number of leaders can be specified in the algorithm, the summation of coordinates is performed in a loop. After that, the resulting amount is divided by the number of leaders. We carry out this operation for each coordinate separately, each time generating new r1 and r2 values. As we can see, the new position of the omega wolves is adjusted according to the positions of the leaders taking into account their own current position.
//----------------------------------------------------------------------------
double a  = sqrt (2.0 * (1.0 - (epochNow / epochCount)));
double r1 = 0.0;
double r2 = 0.0;

double Ai = 0.0;
double Ci = 0.0;
double Xn = 0.0;

double min = 0.0;
double max = 1.0;

//omega-----------------------------------------------------------------------
for (int w = alphaNumber; w < wolvesNumber; w++)
{
  Xn = 0.0;

  for (int c = 0; c < coordinates; c++)
  {
    for (int abd = 0; abd < alphaNumber; abd++)
    {
      r1 = RNDfromCI (min, max);
      r2 = RNDfromCI (min, max);
      Ai = 2.0 * a * r1 - a;
      Ci = 2.0 * r2;
      Xn += wolves [abd].c [c] - Ai * (Ci * wolves [abd].c [c] - wolves [w].c [c]);
    }

    wolves [w].c [c] = Xn /= (double)alphaNumber;
  }

  ReturnToRange (wolves [w]);
}

Here is the calculation of the leaders. The a, Ai and Ci ratios per each coordinate are calculated for them. The only difference is that the position of the leaders changes in relation to the best prey coordinates at the current moment and their own positions. The leaders circle around the prey, moving in and out, and control the minor wolves in the attack.

//alpha, beta, delta----------------------------------------------------------
for (int w = 0; w < alphaNumber; w++)
{
  for (int c = 0; c < coordinates; c++)
  {
    r1 = RNDfromCI (min, max);
    r2 = RNDfromCI (min, max);

    Ai = 2.0 * a * r1 - a;
    Ci = 2.0 * r2;

    wolves [w].c [c] = cB [c] - Ai * (Ci * cB [c] - wolves [w].c [c]);
  }

  ReturnToRange (wolves [w]);
}

This is the second public method called on each iteration. The status of the leaders in the pack is updated here. In fact, the wolves are sorted by fitness value. If better prey coordinates are found than those stored throughout the swarm, then we update the values.

//——————————————————————————————————————————————————————————————————————————————
void C_AO_GWO::RevisionAlphaStatus ()
{
  SortingWolves ();

  if (wolves [0].p > pB)
  {
    pB = wolves [0].p;
    ArrayCopy (cB, wolves [0].c, 0, 0, WHOLE_ARRAY);
  }
}
//——————————————————————————————————————————————————————————————————————————————


3. Test functions

You already know the Skin, Forest and Megacity functions. These test functions satisfy all the complexity criteria for testing optimization algorithms. However, there is one feature that was not taken into account. It should be implemented to increase the test objectivity. The requirements are as follows:

  1. The global extremum should not be on the borders of the range. If the algorithm does not have an out-of-range check, then situations are possible when the algorithm will show excellent results. This is due to the fact that the values will be located on the borders due to an internal defect.
  2. The global extremum should not be in the center of the range coordinates. In this case, the algorithm, generating values averaged in a range, is taken into account.
  3. The global minimum should be located in the center of coordinates. This is necessary in order to deliberately exclude the situations described in the p. 2.
  4. The calculation of the results of the test function should take into account the case, in which randomly generated numbers over the entire domain of the function (when the function is multivariable) will give an average result of about 50% of the maximum, although in fact these results were obtained by chance.

Taking into account these requirements, the boundaries of the test functions were revised, and the centers of the range were shifted to the minimums of the function values. Let me summarize once again. Doing this was necessary in order to obtain the greatest plausibility and objectivity of the results of the test optimization algorithms. Therefore, on new test functions, the optimization algorithm based on random number generation showed a naturally low overall result. The updated rating table is located at the end of the article.

Skin function. A smooth function that has several local extrema that can confuse the optimization algorithm as it can get stuck in one of them. The only global extremum is characterized by weakly changing values in the vicinity. This function clearly shows the ability of the algorithm to be divided into areas under study, rather than focusing on a single one. In particular, the bee colony (ABC) algorithm behaves this way.

skin

Fig. 4. Skin test function

Forest function. A function with several smooth and several non-differentiable extrema. This is a worthy test of optimization algorithms for the ability to find a "needle in a haystack". Finding a single global maximum point is a very difficult task, especially if the function contains many variables. The ant colony algorithm (ACO) distinguished itself in this task by the characteristic behavior, which paves paths to the goal in an incredible way.



forest

Figure 5 Forest test function

Megacity function. The function is a discrete optimization problem with one global and several local extrema. Extremely complex surface to study provides a good test of algorithms that require a gradient. An additional complexity is added by a completely even "floor", which is also a minimum, which does not give any information about the possible direction towards the global maximum.


megacity

Figure 6 Megacity test function

Checks of incoming arguments for out-of-range values have been added to the code of test functions. In the previous versions of the functions, optimization algorithms could unfairly obtain function values larger than the function actually has within the range of its definition.


4. Test results

Due to the changes made to the test functions, the test stand has also been updated. On the right side of the stand screen, you can still see the convergence graphs of the optimization algorithms. The green line stands for the results of convergence on functions with two variables. The blue line stands for the functions with 40 variables. The red one means functions with 1000 variables. The larger black circle indicates the position of the global maximum of the function. The smaller black circle indicates the position of the current optimization algorithm solution value. The crosshair of white lines indicates the geometric center of the test functions and corresponds to the global minimum. This has been introduced for a better visual perception of the behavior of the tested algorithms. White dots indicate averaged intermediate solutions. Colored small dots indicate pairs of coordinates of the corresponding dimension. The color indicates the ordinal position of the dimension of the test function.

The updated results of testing the optimization algorithms discussed in previous articles on the new stand can be seen in the updated table. For more visual clarity, the line about the speed of convergence has been removed from the table - it can be visually determined on the animation of the stand. Added the column with the algorithm description.

ACOm (Ant Colony Optimization) test results:

2022.11.28 12:17:00.468    Test_AO_ACO (EURUSD,M1)    =============================
2022.11.28 12:17:06.382    Test_AO_ACO (EURUSD,M1)    1 Skin's; Func runs 10000 result: 4.844203223078298
2022.11.28 12:17:06.382    Test_AO_ACO (EURUSD,M1)    Score: 0.98229
2022.11.28 12:17:14.191    Test_AO_ACO (EURUSD,M1)    20 Skin's; Func runs 10000 result: 4.043383610736287
2022.11.28 12:17:14.191    Test_AO_ACO (EURUSD,M1)    Score: 0.79108
2022.11.28 12:17:55.578    Test_AO_ACO (EURUSD,M1)    500 Skin's; Func runs 10000 result: 1.2580170651681026
2022.11.28 12:17:55.578    Test_AO_ACO (EURUSD,M1)    Score: 0.12602
2022.11.28 12:17:55.578    Test_AO_ACO (EURUSD,M1)    =============================
2022.11.28 12:18:01.491    Test_AO_ACO (EURUSD,M1)    1 Forest's; Func runs 10000 result: 1.7678766100234538
2022.11.28 12:18:01.491    Test_AO_ACO (EURUSD,M1)    Score: 1.00000
2022.11.28 12:18:09.508    Test_AO_ACO (EURUSD,M1)    20 Forest's; Func runs 10000 result: 1.0974381500585855
2022.11.28 12:18:09.508    Test_AO_ACO (EURUSD,M1)    Score: 0.62077
2022.11.28 12:18:53.348    Test_AO_ACO (EURUSD,M1)    500 Forest's; Func runs 10000 result: 0.20367726028454042
2022.11.28 12:18:53.348    Test_AO_ACO (EURUSD,M1)    Score: 0.11521
2022.11.28 12:18:53.348    Test_AO_ACO (EURUSD,M1)    =============================
2022.11.28 12:18:59.303    Test_AO_ACO (EURUSD,M1)    1 Megacity's; Func runs 10000 result: 4.6
2022.11.28 12:18:59.303    Test_AO_ACO (EURUSD,M1)    Score: 0.38333
2022.11.28 12:19:07.598    Test_AO_ACO (EURUSD,M1)    20 Megacity's; Func runs 10000 result: 5.28
2022.11.28 12:19:07.598    Test_AO_ACO (EURUSD,M1)    Score: 0.44000
2022.11.28 12:19:53.172    Test_AO_ACO (EURUSD,M1)    500 Megacity's; Func runs 10000 result: 0.2852
2022.11.28 12:19:53.172    Test_AO_ACO (EURUSD,M1)    Score: 0.02377
2022.11.28 12:19:53.172    Test_AO_ACO (EURUSD,M1)    =============================
2022.11.28 12:19:53.172    Test_AO_ACO (EURUSD,M1)    All score for C_AO_ACOm: 0.4980520084646583

(Artificial Bee Colony) test results:

2022.11.28 12:35:47.181    Test_AO_ABCm (EURUSD,M1)    =============================
2022.11.28 12:35:52.581    Test_AO_ABCm (EURUSD,M1)    1 Skin's; Func runs 10000 result: 4.918379986612587
2022.11.28 12:35:52.581    Test_AO_ABCm (EURUSD,M1)    Score: 1.00000
2022.11.28 12:35:59.454    Test_AO_ABCm (EURUSD,M1)    20 Skin's; Func runs 10000 result: 3.4073825805846374
2022.11.28 12:35:59.454    Test_AO_ABCm (EURUSD,M1)    Score: 0.63922
2022.11.28 12:36:32.428    Test_AO_ABCm (EURUSD,M1)    500 Skin's; Func runs 10000 result: 1.0684464927353337
2022.11.28 12:36:32.428    Test_AO_ABCm (EURUSD,M1)    Score: 0.08076
2022.11.28 12:36:32.428    Test_AO_ABCm (EURUSD,M1)    =============================
2022.11.28 12:36:38.086    Test_AO_ABCm (EURUSD,M1)    1 Forest's; Func runs 10000 result: 1.766245456669898
2022.11.28 12:36:38.086    Test_AO_ABCm (EURUSD,M1)    Score: 0.99908
2022.11.28 12:36:45.326    Test_AO_ABCm (EURUSD,M1)    20 Forest's; Func runs 10000 result: 0.35556125136004335
2022.11.28 12:36:45.326    Test_AO_ABCm (EURUSD,M1)    Score: 0.20112
2022.11.28 12:37:22.301    Test_AO_ABCm (EURUSD,M1)    500 Forest's; Func runs 10000 result: 0.06691711149962026
2022.11.28 12:37:22.301    Test_AO_ABCm (EURUSD,M1)    Score: 0.03785
2022.11.28 12:37:22.301    Test_AO_ABCm (EURUSD,M1)    =============================
2022.11.28 12:37:28.047    Test_AO_ABCm (EURUSD,M1)    1 Megacity's; Func runs 10000 result: 12.0
2022.11.28 12:37:28.047    Test_AO_ABCm (EURUSD,M1)    Score: 1.00000
2022.11.28 12:37:35.689    Test_AO_ABCm (EURUSD,M1)    20 Megacity's; Func runs 10000 result: 1.9600000000000002
2022.11.28 12:37:35.689    Test_AO_ABCm (EURUSD,M1)    Score: 0.16333
2022.11.28 12:38:11.609    Test_AO_ABCm (EURUSD,M1)    500 Megacity's; Func runs 10000 result: 0.33880000000000005
2022.11.28 12:38:11.609    Test_AO_ABCm (EURUSD,M1)    Score: 0.02823
2022.11.28 12:38:11.609    Test_AO_ABCm (EURUSD,M1)    =============================
2022.11.28 12:38:11.609    Test_AO_ABCm (EURUSD,M1)    All score for C_AO_ABCm: 0.4610669021761763

ABC (Artificial Bee Colony) test results:

2022.11.28 12:29:51.177    Test_AO_ABC (EURUSD,M1)    =============================
2022.11.28 12:29:56.785    Test_AO_ABC (EURUSD,M1)    1 Skin's; Func runs 10000 result: 4.890679983950205
2022.11.28 12:29:56.785    Test_AO_ABC (EURUSD,M1)    Score: 0.99339
2022.11.28 12:30:03.880    Test_AO_ABC (EURUSD,M1)    20 Skin's; Func runs 10000 result: 3.8035430744604133
2022.11.28 12:30:03.880    Test_AO_ABC (EURUSD,M1)    Score: 0.73381
2022.11.28 12:30:37.089    Test_AO_ABC (EURUSD,M1)    500 Skin's; Func runs 10000 result: 1.195840100227333
2022.11.28 12:30:37.089    Test_AO_ABC (EURUSD,M1)    Score: 0.11118
2022.11.28 12:30:37.089    Test_AO_ABC (EURUSD,M1)    =============================
2022.11.28 12:30:42.811    Test_AO_ABC (EURUSD,M1)    1 Forest's; Func runs 10000 result: 1.7667070507449298
2022.11.28 12:30:42.811    Test_AO_ABC (EURUSD,M1)    Score: 0.99934
2022.11.28 12:30:50.108    Test_AO_ABC (EURUSD,M1)    20 Forest's; Func runs 10000 result: 0.3789854806095275
2022.11.28 12:30:50.108    Test_AO_ABC (EURUSD,M1)    Score: 0.21437
2022.11.28 12:31:25.900    Test_AO_ABC (EURUSD,M1)    500 Forest's; Func runs 10000 result: 0.07451308481273813
2022.11.28 12:31:25.900    Test_AO_ABC (EURUSD,M1)    Score: 0.04215
2022.11.28 12:31:25.900    Test_AO_ABC (EURUSD,M1)    =============================
2022.11.28 12:31:31.510    Test_AO_ABC (EURUSD,M1)    1 Megacity's; Func runs 10000 result: 10.2
2022.11.28 12:31:31.510    Test_AO_ABC (EURUSD,M1)    Score: 0.85000
2022.11.28 12:31:38.855    Test_AO_ABC (EURUSD,M1)    20 Megacity's; Func runs 10000 result: 2.02
2022.11.28 12:31:38.855    Test_AO_ABC (EURUSD,M1)    Score: 0.16833
2022.11.28 12:32:14.623    Test_AO_ABC (EURUSD,M1)    500 Megacity's; Func runs 10000 result: 0.37559999999999993
2022.11.28 12:32:14.623    Test_AO_ABC (EURUSD,M1)    Score: 0.03130
2022.11.28 12:32:14.623    Test_AO_ABC (EURUSD,M1)    =============================
2022.11.28 12:32:14.623    Test_AO_ABC (EURUSD,M1)    All score for C_AO_ABC: 0.46043003186219245

PSO (Particle Swarm Optimization) test results

2022.11.28 12:01:03.967    Test_AO_PSO (EURUSD,M1)    =============================
2022.11.28 12:01:09.723    Test_AO_PSO (EURUSD,M1)    1 Skin's; Func runs 10000 result: 4.90276049713715
2022.11.28 12:01:09.723    Test_AO_PSO (EURUSD,M1)    Score: 0.99627
2022.11.28 12:01:17.064    Test_AO_PSO (EURUSD,M1)    20 Skin's; Func runs 10000 result: 2.3250668562024566
2022.11.28 12:01:17.064    Test_AO_PSO (EURUSD,M1)    Score: 0.38080
2022.11.28 12:01:52.880    Test_AO_PSO (EURUSD,M1)    500 Skin's; Func runs 10000 result: 0.943331687769892
2022.11.28 12:01:52.881    Test_AO_PSO (EURUSD,M1)    Score: 0.05089
2022.11.28 12:01:52.881    Test_AO_PSO (EURUSD,M1)    =============================
2022.11.28 12:01:58.492    Test_AO_PSO (EURUSD,M1)    1 Forest's; Func runs 10000 result: 1.6577769478566602
2022.11.28 12:01:58.492    Test_AO_PSO (EURUSD,M1)    Score: 0.93772
2022.11.28 12:02:06.105    Test_AO_PSO (EURUSD,M1)    20 Forest's; Func runs 10000 result: 0.25704414127018393
2022.11.28 12:02:06.105    Test_AO_PSO (EURUSD,M1)    Score: 0.14540
2022.11.28 12:02:44.566    Test_AO_PSO (EURUSD,M1)    500 Forest's; Func runs 10000 result: 0.08584805450831333
2022.11.28 12:02:44.566    Test_AO_PSO (EURUSD,M1)    Score: 0.04856
2022.11.28 12:02:44.566    Test_AO_PSO (EURUSD,M1)    =============================
2022.11.28 12:02:50.268    Test_AO_PSO (EURUSD,M1)    1 Megacity's; Func runs 10000 result: 12.0
2022.11.28 12:02:50.268    Test_AO_PSO (EURUSD,M1)    Score: 1.00000
2022.11.28 12:02:57.649    Test_AO_PSO (EURUSD,M1)    20 Megacity's; Func runs 10000 result: 1.1199999999999999
2022.11.28 12:02:57.649    Test_AO_PSO (EURUSD,M1)    Score: 0.09333
2022.11.28 12:03:34.895    Test_AO_PSO (EURUSD,M1)    500 Megacity's; Func runs 10000 result: 0.268
2022.11.28 12:03:34.895    Test_AO_PSO (EURUSD,M1)    Score: 0.02233
2022.11.28 12:03:34.895    Test_AO_PSO (EURUSD,M1)    =============================
2022.11.28 12:03:34.895    Test_AO_PSO (EURUSD,M1)    All score for C_AO_PSO: 0.40836715689743186

RND (Random) test results:

2022.11.28 16:45:15.976    Test_AO_RND (EURUSD,M1)    =============================
2022.11.28 16:45:21.569    Test_AO_RND (EURUSD,M1)    1 Skin's; Func runs 10000 result: 4.915522750114194
2022.11.28 16:45:21.569    Test_AO_RND (EURUSD,M1)    Score: 0.99932
2022.11.28 16:45:28.607    Test_AO_RND (EURUSD,M1)    20 Skin's; Func runs 10000 result: 2.584546688199847
2022.11.28 16:45:28.607    Test_AO_RND (EURUSD,M1)    Score: 0.44276
2022.11.28 16:46:02.695    Test_AO_RND (EURUSD,M1)    500 Skin's; Func runs 10000 result: 1.0161336237263792
2022.11.28 16:46:02.695    Test_AO_RND (EURUSD,M1)    Score: 0.06827
2022.11.28 16:46:02.695    Test_AO_RND (EURUSD,M1)    =============================
2022.11.28 16:46:09.622    Test_AO_RND (EURUSD,M1)    1 Forest's; Func runs 10000 result: 1.4695680943894533
2022.11.28 16:46:09.622    Test_AO_RND (EURUSD,M1)    Score: 0.83126
2022.11.28 16:46:17.675    Test_AO_RND (EURUSD,M1)    20 Forest's; Func runs 10000 result: 0.20373533112604475
2022.11.28 16:46:17.675    Test_AO_RND (EURUSD,M1)    Score: 0.11524
2022.11.28 16:46:54.544    Test_AO_RND (EURUSD,M1)    500 Forest's; Func runs 10000 result: 0.0538909816827325
2022.11.28 16:46:54.544    Test_AO_RND (EURUSD,M1)    Score: 0.03048
2022.11.28 16:46:54.544    Test_AO_RND (EURUSD,M1)    =============================
2022.11.28 16:47:00.219    Test_AO_RND (EURUSD,M1)    1 Megacity's; Func runs 10000 result: 10.0
2022.11.28 16:47:00.219    Test_AO_RND (EURUSD,M1)    Score: 0.83333
2022.11.28 16:47:08.145    Test_AO_RND (EURUSD,M1)    20 Megacity's; Func runs 10000 result: 1.08
2022.11.28 16:47:08.145    Test_AO_RND (EURUSD,M1)    Score: 0.09000
2022.11.28 16:47:49.875    Test_AO_RND (EURUSD,M1)    500 Megacity's; Func runs 10000 result: 0.28840000000000005
2022.11.28 16:47:49.875    Test_AO_RND (EURUSD,M1)    Score: 0.02403
2022.11.28 16:47:49.875    Test_AO_RND (EURUSD,M1)    =============================
2022.11.28 16:47:49.875    Test_AO_RND (EURUSD,M1)    All score for C_AO_RND: 0.38163317904126015



skin

  GWO on the Skin test function

forest

GWO on the Forest test function

megacity

  GWO on the Megacity test function

GWO test results.

2022.11.28 13:24:09.370    Test_AO_GWO (EURUSD,M1)    =============================
2022.11.28 13:24:14.895    Test_AO_GWO (EURUSD,M1)    1 Skin's; Func runs 10000 result: 4.914175888065222
2022.11.28 13:24:14.895    Test_AO_GWO (EURUSD,M1)    Score: 0.99900
2022.11.28 13:24:22.175    Test_AO_GWO (EURUSD,M1)    20 Skin's; Func runs 10000 result: 2.7419092435309405
2022.11.28 13:24:22.175    Test_AO_GWO (EURUSD,M1)    Score: 0.48033
2022.11.28 13:25:01.381    Test_AO_GWO (EURUSD,M1)    500 Skin's; Func runs 10000 result: 1.5227848592798188
2022.11.28 13:25:01.381    Test_AO_GWO (EURUSD,M1)    Score: 0.18924
2022.11.28 13:25:01.381    Test_AO_GWO (EURUSD,M1)    =============================
2022.11.28 13:25:06.924    Test_AO_GWO (EURUSD,M1)    1 Forest's; Func runs 10000 result: 1.4822580151819842
2022.11.28 13:25:06.924    Test_AO_GWO (EURUSD,M1)    Score: 0.83844
2022.11.28 13:25:14.551    Test_AO_GWO (EURUSD,M1)    20 Forest's; Func runs 10000 result: 0.15477395149266915
2022.11.28 13:25:14.551    Test_AO_GWO (EURUSD,M1)    Score: 0.08755
2022.11.28 13:25:56.900    Test_AO_GWO (EURUSD,M1)    500 Forest's; Func runs 10000 result: 0.04517298232457319
2022.11.28 13:25:56.900    Test_AO_GWO (EURUSD,M1)    Score: 0.02555
2022.11.28 13:25:56.900    Test_AO_GWO (EURUSD,M1)    =============================
2022.11.28 13:26:02.305    Test_AO_GWO (EURUSD,M1)    1 Megacity's; Func runs 10000 result: 12.0
2022.11.28 13:26:02.305    Test_AO_GWO (EURUSD,M1)    Score: 1.00000
2022.11.28 13:26:09.475    Test_AO_GWO (EURUSD,M1)    20 Megacity's; Func runs 10000 result: 1.2
2022.11.28 13:26:09.475    Test_AO_GWO (EURUSD,M1)    Score: 0.10000
2022.11.28 13:26:48.980    Test_AO_GWO (EURUSD,M1)    500 Megacity's; Func runs 10000 result: 0.2624
2022.11.28 13:26:48.980    Test_AO_GWO (EURUSD,M1)    Score: 0.02187
2022.11.28 13:26:48.980    Test_AO_GWO (EURUSD,M1)    =============================
2022.11.28 13:26:48.980    Test_AO_GWO (EURUSD,M1)    All score for C_AO_GWO: 0.41577484361261224

The Gray Wolves Optimization Algorithm (GWO) is one of the recent bio-inspired optimization algorithms based on the simulated hunting of a pack of gray wolves. On average, the algorithm has proven itself quite efficient on various types of functions, both in terms of the accuracy of finding an extremum and in terms of the speed of convergence. In some tests, it has turned out to be the best. The main performance indicators of the "Gray Wolves" optimization algorithm are better than those of the particle swarm optimization algorithm, which is believed to be "conventional" in the class of bio-inspired optimization algorithms.

At the same time, the computational complexity of the Gray Wolves optimization algorithm is comparable to the particle swarm optimization algorithm. Due to numerous advantages of the GWO optimization algorithm, many works on its modification have appeared in a short time since the algorithm was first published. The only drawback of the algorithm is the low accuracy of the found coordinates of the Forest sharp-maximum function.

The low accuracy of the found extremum manifested itself in all dimensions of the Forest function, and the results are the worst among all participants in the table. The algorithm proved to be efficient on the smooth Skin function, especially in case of the larger dimension Skin function. GWO is also third in the table to achieve a 100% hit in the global maximum on the Megacity function.

AO

Description

Skin

Forest

Megacity (discrete)

Final result

2 params (1 F)

40 params (20 F)

1000 params (500 F)

2 params (1 F)

40 params (20 F)

1000 params (500 F)

2 params (1 F)

40 params (20 F)

1000 params (500 F)

ACOm

ant colony optimization

0.98229

0.79108

0.12602

1.00000

0.62077

0.11521

0.38333

0.44000

0.02377

0.49805222

ABCm

artificial bee colony M

1.00000

0.63922

0.08076

0.99908

0.20112

0.03785

1.00000

0.16333

0.02823

0.46106556

ABC

artificial bee colony

0.99339

0.73381

0.11118

0.99934

0.21437

0.04215

0.85000

0.16833

0.03130

0.46043000

GWO

grey wolf optimizer

0.99900

0.48033

0.18924

0.83844

0.08755

0.02555

1.00000

0.10000

0.02187

0.41577556

PSO

particle swarm optimisation

0.99627

0.38080

0.05089

0.93772

0.14540

0.04856

1.00000

0.09333

0.02233

0.40836667

RND

random

0.99932

0.44276

0.06827

0.83126

0.11524

0.03048

0.83333

0.09000

0.02403

0.38163222


Conclusions:

Pros:
1. High speed.
2. High convergence for smooth functions with a large number of variables.

Cons:
1. Not universal.
2. Getting stuck in local extremes.
3. Low scalability on discrete and non-differentiable functions.


Translated from Russian by MetaQuotes Ltd.
Original article: https://www.mql5.com/ru/articles/11785

Attached files |
Last comments | Go to discussion (1)
Smarterbot Software
Vinicius Barenho Pereira | 27 Jan 2023 at 18:42

Those articles about metaheuristic optimization techniques are awesome! You are doing a great job Andrey, it's mind blowing how much experience you have to share with us, thank you!

@METAQUOTES please consider implement those metaheuristic optimization targets to the optimizer! It would be great for the software.

Something easy that user can set inside OnTester() as:

OptimizerSetEngine("ACO"); // Ant Colony Optimization
OptimizerSetEngine("COA"); // cuckoo optimization algorithm
OptimizerSetEngine("ABC"); // artificial bee colony
OptimizerSetEngine("GWO"); // grey wolf optimizer
OptimizerSetEngine("PSO"); // particle swarm optimisation 



Cheers from Brazil

Data Science and Machine Learning (Part 10): Ridge Regression Data Science and Machine Learning (Part 10): Ridge Regression
Ridge regression is a simple technique to reduce model complexity and prevent over-fitting which may result from simple linear regression
Population optimization algorithms: Artificial Bee Colony (ABC) Population optimization algorithms: Artificial Bee Colony (ABC)
In this article, we will study the algorithm of an artificial bee colony and supplement our knowledge with new principles of studying functional spaces. In this article, I will showcase my interpretation of the classic version of the algorithm.
DoEasy. Controls (Part 28): Bar styles in the ProgressBar control DoEasy. Controls (Part 28): Bar styles in the ProgressBar control
In this article, I will develop display styles and description text for the progress bar of the ProgressBar control.
Non-linear indicators Non-linear indicators
In this article, I will make an attempt to consider some ways of building non-linear indicators and their use in trading. There are quite a few indicators in the MetaTrader trading platform that use non-linear approaches.