Simulated Annealing

Simulated annealing solver for derivative-free unconstrained optimization or optimization with bounds

Use simulated annealing when other solvers don't satisfy you.

Functions

simulannealbnd Find minimum of function using simulated annealing algorithm

`optimoptions`	Create optimization options
`resetoptions`	Reset options

Optimize or solve equations in the Live Editor

Explore multiple solver configurations and their solutions for an optimization problem (Since R2026a)

Optimize Function Using simulannealbnd, Problem-Based
Basic example minimizing a function in the problem-based approach.

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 the simulannealbnd solver. 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.

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.