gistlib - simulated annealing starting point of 9 variables to minimize fitness function shoulb be in matlab

To implement simulated annealing for minimizing a fitness function with 9 variables in MATLAB, you can follow these steps:

Define your fitness function: Create a MATLAB function that calculates the fitness value given the 9 variables as input.


main.m
function fitness = calculateFitness(variables)
    % Your fitness calculation logic here
    % Return the fitness value
end
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Initialize the starting point: Generate a random initial solution for the 9 variables.


main.m
initialSolution = rand(1, 9);
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Define the neighborhood function: Implement a function that generates a neighbor solution by making small perturbations to the current solution.


main.m
function neighbor = getNeighbor(solution)
    % Your neighborhood generation logic here
    % Return a neighbor solution
end
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Implement the simulated annealing algorithm: Write a MATLAB script that implements the simulated annealing algorithm, using the fitness function, initial solution, and neighborhood function defined above.


main.m
initialTemperature = 100;  % Initial temperature
coolingRate = 0.95;        % Cooling rate
finalTemperature = 0.1;    % Final temperature
numIterations = 1000;      % Number of iterations per temperature

currentSolution = initialSolution;
bestSolution = currentSolution;
currentFitness = calculateFitness(currentSolution);
bestFitness = currentFitness;
currentTemperature = initialTemperature;

while currentTemperature > finalTemperature
    for i = 1:numIterations
        neighborSolution = getNeighbor(currentSolution);
        neighborFitness = calculateFitness(neighborSolution);

        % Check if the neighbor is better or accept it with a certain probability
        if neighborFitness < currentFitness
            currentSolution = neighborSolution;
            currentFitness = neighborFitness;
            
            if neighborFitness < bestFitness
                bestSolution = neighborSolution;
                bestFitness = neighborFitness;
            end
        else
            acceptanceProbability = exp((currentFitness - neighborFitness) / currentTemperature);
            if rand() < acceptanceProbability
                currentSolution = neighborSolution;
                currentFitness = neighborFitness;
            end
        end
    end
    
    currentTemperature = currentTemperature * coolingRate;
end

disp('Best solution:');
disp(bestSolution);
disp('Best fitness:');
disp(bestFitness);
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Make sure to replace the fitness calculation logic and neighborhood generation logic in the above code with your specific requirements.

Run the simulated annealing algorithm: Call the MATLAB script to run the simulated annealing algorithm and obtain the best solution and fitness.


main.m
simulatedAnnealing();
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This code will start the simulated annealing algorithm with the defined starting point, and over iterations, it will explore the solution space, gradually reducing the temperature and optimizing the fitness function.

Note: The fitness function, neighborhood function, and other parameters should be customized according to your specific problem.

simulated annealing starting point of 9 variables to minimize fitness function shoulb be in matlab

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