Problem-Based Simulated Annealing

Optimize Function Using simulannealbnd, Problem-Based

Basic example minimizing a function in the problem-based approach.

Optimize Using Simulated Annealing

Minimize Function with Many Local Minima

Presents an example of solving an optimization problem using simulated annealing.

Minimization Using Simulated Annealing Algorithm

This example shows how to create and minimize an objective function using thesimulannealbndsolver. It also shows how to include extra parameters for the minimization.

Simulated Annealing Options

Shows the effects of some options on the simulated annealing solution process.

Multiprocessor Scheduling Using Simulated Annealing with a Custom Data Type

Uses a custom data type to code a scheduling problem. Uses a custom plot function to monitor the optimization process.

Reproduce Your Results

Explains how to obtain identical results by setting the random seed.

When to Use a Hybrid Function

Describes cases where hybrid functions are likely to provide greater accuracy or speed.

Simulated Annealing Background

What Is Simulated Annealing?

Introduces simulated annealing.

Simulated Annealing Terminology

Explains some basic terminology for simulated annealing.

How Simulated Annealing Works

Presents an overview of how the simulated annealing algorithm works.

Simulated Annealing Options

Explore the options for simulated annealing.