how to write simple simulated annealing code to minimize fitness function that contain sum of three metrics in matlab

You can write a simple simulated annealing code in MATLAB to minimize a fitness function that contains the sum of three metrics. Here is an example implementation:

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

% Define the number of variables and their bounds
nVars = 3; % Replace with the number of variables you want to optimize
lb = [lowerBound1; lowerBound2; lowerBound3]; % Replace with lower bounds of your variables
ub = [upperBound1; upperBound2; upperBound3]; % Replace with upper bounds of your variables

% Define the options for simulated annealing
options = saoptimset('Display', 'off');

% Run the simulated annealing optimization
[xOpt, fOpt] = simulannealbnd(fitness, lb, ub, options);

% Display the optimal solution and fitness
disp('Optimal solution:');
disp(xOpt);
disp('Optimal fitness:');
disp(fOpt);
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In this code, yourFitnessFunction should be replaced with your actual fitness function that computes the sum of three metrics based on the input variables x. The number of variables (nVars), lower bounds (lb), and upper bounds (ub) should be defined according to the specific problem you are solving.

The simulannealbnd function is used to run the simulated annealing optimization. It takes the fitness function, variable bounds, and options as inputs, and returns the optimal solution (xOpt) and the corresponding fitness value (fOpt).

Remember to replace the placeholders with your actual fitness function and variable information.