Get Started with Global Optimization Toolbox
Global Optimization Toolbox provides functions that search for global solutions to problems that contain multiple maxima or minima. Toolbox solvers include surrogate, pattern search, genetic algorithm, particle swarm, simulated annealing, multistart, and global search. You can use these solvers for optimization problems where the objective or constraint function is continuous, discontinuous, stochastic, does not possess derivatives, or includes simulations or black-box functions. For problems with multiple objectives, you can identify a Pareto front using genetic algorithm or pattern search solvers.
You can improve solver effectiveness by adjusting options and, for applicable solvers, customizing creation, update, and search functions. You can use custom data types with the genetic algorithm and simulated annealing solvers to represent problems not easily expressed with standard data types. The hybrid function option lets you improve a solution by applying a second solver after the first.
- Decide Between Problem-Based and Solver-Based Approach
Explore considerations for problem-based and solver-based optimization with Global Optimization Toolbox solvers.
- Compare Several Global Solvers, Problem-Based
Example showing some characteristics of global solvers.
- Comparison of Six Solvers
Explore some characteristics of global solvers.
- Solver Behavior with a Nonsmooth Problem
Demonstrates the importance of choosing an appropriate solver.
About Global Optimization
- What Is Global Optimization?
Defines global vs local solutions, and basins of attraction.
- Can You Certify That a Solution Is Global?
Issues in determining whether a solution is good.
- Optimization Workflow
How to find a local or global optimum.