Here is an example of how to implement the simulated annealing algorithm for a problem with 9 variables in MATLAB:
main.m1845 chars62 lines
This code assumes a simple objective function where the goal is to minimize the sum of squares of the variables. Adjust the objective function according to your specific problem.
Make sure to specify the correct bounds for your problem by modifying the lb
(lower bound) and ub
(upper bound) vectors.
The code also specifies the maximum number of iterations, initial temperature, cooling rate, and minimum temperature. Adjust these parameters based on the specific requirements of your problem.
Note that this implementation uses a simple Gaussian perturbation to generate new candidate solutions. You may need to modify this part for a problem with different characteristics.
Keep in mind that simulated annealing is a randomized algorithm, so you may obtain slightly different results each time you run it.
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