simulated annealing like genetic algorithm code in matlab

main.m
% Define the objective function to be optimized
objectiveFunction = @(x) x^2;  % Replace with your objective function

% Define the search space and bounds
lowerBound = -10;  % Lower bound of the search space
upperBound = 10;   % Upper bound of the search space

% Simulated Annealing parameters
initialTemperature = 100;   % Initial temperature
coolingRate = 0.95;         % Cooling rate
iterationsPerTemperature = 100;   % Iterations per temperature

% Initialize the current solution
currentSolution = (upperBound - lowerBound) * rand() + lowerBound;

% Initialize the current and best objective function values
currentObjValue = objectiveFunction(currentSolution);
bestObjValue = currentObjValue;

% Simulated Annealing
currentTemperature = initialTemperature;

while currentTemperature > 1e-6  % Stopping criterion
    for i = 1:iterationsPerTemperature
        % Generate a random candidate solution
        candidateSolution = (upperBound - lowerBound) * rand() + lowerBound;
        
        % Calculate the objective function value for the candidate solution
        candidateObjValue = objectiveFunction(candidateSolution);
        
        % Calculate the acceptance probability
        acceptanceProbability = exp((currentObjValue - candidateObjValue) / currentTemperature);
        
        % Accept or reject the candidate solution
        if rand() < acceptanceProbability
            currentSolution = candidateSolution;
            currentObjValue = candidateObjValue;
        end
        
        % Update the best solution if necessary
        if currentObjValue < bestObjValue
            bestSolution = currentSolution;
            bestObjValue = currentObjValue;
        end
    end
    
    % Cool down the temperature
    currentTemperature = currentTemperature * coolingRate;
end

% Display the best solution and its objective function value
disp('Best Solution:');
disp(bestSolution);
disp('Objective Function Value:');
disp(bestObjValue);
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