How to adjust and adapt General code of Metaheuristic algorithm to fit you problem?

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YASSER
YASSER 2024-4-19
回答： Umang Pandey 2024-7-18
Dear all;
I am using a standard algorithm of GWO to implement it of parameters estimation of my problem
The general GWO code is based on Objective function, in my case i do not have the objective function. instead, i have experiment data.
I need the algorithm frameworks (pseaudo code or flowchart ..) in this case to use it for multiple metaheuristic cases.
Here is the general code:
% Grey Wolf Optimizer
function [Alpha_score,Alpha_pos,Convergence_curve]=GWO(SearchAgents_no,Max_iter,lb,ub,dim,fobj)
% initialize alpha, beta, and delta_pos
Alpha_pos=zeros(1,dim);
Alpha_score=inf; %change this to -inf for maximization problems
Beta_pos=zeros(1,dim);
Beta_score=inf; %change this to -inf for maximization problems
Delta_pos=zeros(1,dim);
Delta_score=inf; %change this to -inf for maximization problems
%Initialize the positions of search agents
Positions=initialization(SearchAgents_no,dim,ub,lb);
Convergence_curve=zeros(1,Max_iter);
l=0;% Loop counter
% Main loop
while l<Max_iter
for i=1:size(Positions,1)
% Return back the search agents that go beyond the boundaries of the search space
Flag4ub=Positions(i,:)>ub;
Flag4lb=Positions(i,:)<lb;
Positions(i,:)=(Positions(i,:).*(~(Flag4ub+Flag4lb)))+ub.*Flag4ub+lb.*Flag4lb;
% Calculate objective function for each search agent
fitness=fobj(Positions(i,:));
% Update Alpha, Beta, and Delta
if fitness<Alpha_score
Alpha_score=fitness; % Update alpha
Alpha_pos=Positions(i,:);
end
if fitness>Alpha_score && fitness<Beta_score
Beta_score=fitness; % Update beta
Beta_pos=Positions(i,:);
end
if fitness>Alpha_score && fitness>Beta_score && fitness<Delta_score
Delta_score=fitness; % Update delta
Delta_pos=Positions(i,:);
end
end
a=2-l*((2)/Max_iter); % a decreases linearly fron 2 to 0
% Update the Position of search agents including omegas
for i=1:size(Positions,1)
for j=1:size(Positions,2)
r1=rand(); % r1 is a random number in [0,1]
r2=rand(); % r2 is a random number in [0,1]
A1=2*a*r1-a; % Equation (3.3)
C1=2*r2; % Equation (3.4)
D_alpha=abs(C1*Alpha_pos(j)-Positions(i,j)); % Equation (3.5)-part 1
X1=Alpha_pos(j)-A1*D_alpha; % Equation (3.6)-part 1
r1=rand();
r2=rand();
A2=2*a*r1-a; % Equation (3.3)
C2=2*r2; % Equation (3.4)
D_beta=abs(C2*Beta_pos(j)-Positions(i,j)); % Equation (3.5)-part 2
X2=Beta_pos(j)-A2*D_beta; % Equation (3.6)-part 2
r1=rand();
r2=rand();
A3=2*a*r1-a; % Equation (3.3)
C3=2*r2; % Equation (3.4)
D_delta=abs(C3*Delta_pos(j)-Positions(i,j)); % Equation (3.5)-part 3
X3=Delta_pos(j)-A3*D_delta; % Equation (3.5)-part 3
Positions(i,j)=(X1+X2+X3)/3;% Equation (3.7)
end
end
l=l+1;
Convergence_curve(l)=Alpha_score;
end

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回答（1 个）

Umang Pandey
Umang Pandey 2024-7-18
Hi Yasser,
To adapt the Grey Wolf Optimizer (GWO) for parameter estimation using experimental data instead of an explicit objective function, you can create a custom objective function that measures the discrepancy between the model's predictions (based on the current parameters) and the experimental data. This discrepancy can be quantified using a metric such as the mean squared error (MSE).
Here is a pseudo-code framework for the modified GWO algorithm:
1) Initialize Parameters:
  • Number of search agents (SearchAgents_no)
  • Maximum number of iterations (Max_iter)
  • Lower and upper bounds of the parameters (lb, ub)
  • Dimension of the parameter space (dim)
  • Experimental data (exp_data)
2) Initialize Alpha, Beta, and Delta Positions and Scores:
  • Alpha_pos, Beta_pos, Delta_pos as zero vectors of size dim
  • Alpha_score, Beta_score, Delta_score as infinity
3) Initialize the Positions of Search Agents:
  • Positions matrix with random values within bounds lb and ub
4) Main Loop (Iterate until Max_iter):
  • For each search agent:
  • Ensure positions are within bounds
  • Calculate the fitness of each search agent:
  • Use a custom objective function that computes the MSE between model predictions and exp_data
  • Update Alpha, Beta, and Delta positions and scores based on fitness
  • Update the positions of search agents using GWO equations
  • Record the best score in the convergence curve
5) Return Results:
  • Best score (Alpha_score)
  • Best position (Alpha_pos)
  • Convergence curve (Convergence_curve)
You can create a sample Objective function that calculates the MSE between the model's predictions and the experimental data like this:
function mse = customObjectiveFunction(params, exp_data)
% Model predictions based on current params
model_predictions = modelFunction(params);
% Calculate Mean Squared Error
mse = mean((exp_data - model_predictions).^2);
end
function predictions = modelFunction(params)
% Define your model here using the parameters
% For example, a simple linear model:
% predictions = params(1) * x + params(2);
% Replace this with your actual model
predictions = ...;
end
You can refer to the following File Exchange Submission for GWO implementation:
https://www.mathworks.com/matlabcentral/fileexchange/44974-grey-wolf-optimizer-gwo
Hope this helps!
Best,
Umang

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