Simulated Annealing Algorithm

MATLAB 5K 220 views 0 downloads 1 credits
Tags:

Resource Overview

The simulated annealing algorithm is designed to overcome local optima in optimization problems, ensuring the final solution reaches global optimality. This MATLAB implementation includes temperature control, neighbor state generation, and acceptance probability functions to efficiently solve complex optimization tasks in MATLAB environments.

Detailed Documentation

As mentioned in the text, the simulated annealing algorithm is an optimization technique specifically developed to avoid getting trapped in local optima during problem solving. By mimicking the physical annealing process of metals, this algorithm guarantees convergence to a globally optimal solution through controlled temperature reduction and probabilistic acceptance of worse solutions. The MATLAB source code provided implements key components such as: 1) Temperature scheduling using exponential decay functions, 2) Neighborhood search mechanisms for solution space exploration, and 3) Metropolis criterion calculations for determining solution acceptance. This implementation allows researchers to apply simulated annealing directly in MATLAB for various optimization problems including function minimization and combinatorial optimization.

You May Also Like