How to Use Simulated Annealing Solver to Solve Optimization Problems

Hello everyone and welcome! In this post, I’m going to show you how to use Simulated Annealing solver in Matlab to solve optimization problems. It is very easy and effective. Minimum programming skill is required. Almost everyone can do this with a few minutes.

Did you know that Simulated Annealing Algorithm is one of the top three most popular stochastic optimization algorithms for solving complex large scale optimization problems in various fields? Only Genetic Algorithm and Particle Swarm Optimization are more popular than Simulated Annealing Algorithm.

Here are the details of the benchmark function used in this post to test the performance of the Simulated Annealing Solver.

Let’s see how the Simulated Annealing Solver works:

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Matlab code:

function Output = objective_function(Input)
x = Input(1);
y = Input(2);

Output = (sin(3*pi*x))^2 + ((x - 1)^2)*(1 + (sin(3*pi*y))^2)+...
    ((y - 1)^2)*(1 + (sin(2*pi*y))^2);

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