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
Live Editor Tasks
Optimize | Optimize or solve equations in the Live Editor (Since R2020b) |
Topics
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 thesimulannealbnd
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.
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.