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Global Optimization Toolbox

Solve multiple maxima, multiple minima, and nonsmooth optimization problems

Global Optimization Toolbox provides functions that search for global solutions to problems that contain multiple maxima or minima. The toolbox includes global search, multistart, pattern search, genetic algorithm, multiobjective genetic algorithm, simulated annealing, and particle swarm solvers. You can use these solvers to solve optimization problems where the objective or constraint function is continuous, discontinuous, stochastic, does not possess derivatives, or includes simulations or black-box functions.

You can improve solver effectiveness by setting options and 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.

Getting Started

Learn the basics of Global Optimization Toolbox

Optimization Problem Setup

Choose solver, define objective function and constraints, compute in parallel

Global or Multiple Starting Point Search

Multiple starting point solvers for gradient-based optimization, constrained or unconstrained

Direct Search

Pattern search solver for derivative-free optimization, constrained or unconstrained

Genetic Algorithm

Genetic algorithm solver for mixed-integer or continuous-variable optimization, constrained or unconstrained

Particle Swarm

Particle swarm solver for derivative-free unconstrained optimization or optimization with bounds

Simulated Annealing

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

Multiobjective Optimization

Pareto sets via genetic algorithm with or without constraints

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