simulated annealing minimize fitness function of 9 variables matlab in matlab

To minimize the fitness function of 9 variables using simulated annealing in MATLAB, you can follow these steps:

1. Define your fitness function: The fitness function should take a vector of 9 variables as input and return a scalar value representing the fitness or cost.

2. Set up the simulated annealing options: Create an saoptimset object to specify the options for the simulated annealing algorithm. You can adjust parameters such as the initial temperature, cooling schedule, and annealing function.

3. Perform the optimization: Use the simulannealbnd function to perform the simulated annealing optimization. Pass in the fitness function, the initial solution (a vector of 9 variables), and the options object.

Here is an example implementation:

main.m
% Define the fitness function
fitnessFcn = @(x) yourFitnessFunction(x); % Replace 'yourFitnessFunction' with your own fitness function

% Set up simulated annealing options
options = saoptimset('Display', 'final');
options.InitialTemperature = 100;
options.TemperatureFcn = @temperatureexp;
options.CoolingFcn = @coolingexp;
options.MaxIter = 1000;

% Perform optimization
variableCount = 9;
lb = zeros(1, variableCount); % Lower bounds for each variable
ub = ones(1, variableCount); % Upper bounds for each variable
[x,fval] = simulannealbnd(fitnessFcn, initialSolution, lb, ub, options);
590 chars
16 lines

Make sure to replace 'yourFitnessFunction' with the name of your actual fitness function.

Please note that this is a basic example, and you may need to adapt it to fit your specific problem and constraints.