simulated annealing code minimize fitness function with 9 input variables in matlab

To implement the simulated annealing algorithm to minimize a fitness function with 9 input variables in MATLAB, follow these steps:

1. Define your fitness function: This is the function you want to minimize. Let's say it takes a vector of length 9 as input and returns a scalar value indicating the fitness.

   main.m
   function fitness = myFitnessFunction(x)
       % Calculate the fitness based on your problem's logic
       % Return the fitness value as a scalar
   end
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2. Define the initial solution: Generate a random initial solution for the optimization problem. This should be a vector of length 9.

   main.m
   initialSolution = rand(1, 9);
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3. Set the parameters for the simulated annealing algorithm: You need to specify parameters like initial temperature, cooling schedule, and number of iterations.

   main.m
   initialTemperature = 100;   % Initial temperature
   coolingRate = 0.95;         % Cooling rate
   numberOfIterations = 1000;  % Number of iterations
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4. Implement the simulated annealing algorithm: Write a loop that performs the iterations of the algorithm.

   main.m
   currentSolution = initialSolution;
   currentFitness = myFitnessFunction(currentSolution);
   bestSolution = currentSolution;
   bestFitness = currentFitness;
   
   temperature = initialTemperature;
   for i = 1:numberOfIterations
       % Generate a new candidate solution by making a small change to the current solution
       newSolution = currentSolution + randn(1, 9);
   
       % Calculate the fitness of the candidate solution
       newFitness = myFitnessFunction(newSolution);
   
       % Decide whether to accept the candidate solution or not
       if newFitness < currentFitness || exp((currentFitness - newFitness) / temperature) > rand()
           currentSolution = newSolution;
           currentFitness = newFitness;
       end
   
       % Update the best solution if necessary
       if newFitness < bestFitness
           bestSolution = newSolution;
           bestFitness = newFitness;
       end
   
       % Update the temperature
       temperature = temperature * coolingRate;
   end
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5. Access the best solution: After the algorithm finishes, the bestSolution variable will contain the optimal solution found.

   main.m
   disp('Best solution:');
   disp(bestSolution);
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That's it! You have implemented the simulated annealing algorithm to minimize a fitness function with 9 input variables in MATLAB.