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.
Surrogate solver for problems with lengthy objective function execution times and bound constraints
Pattern search solvers for single and multiple objective problems with linear, nonlinear, and bound constraints
Genetic algorithm for problems with linear, nonlinear, bound, and integer constraints
Multiobjective genetic algorithm for problems with linear, nonlinear, and bound constraints
Particle swarm solver for bound constraints
Simulated annealing solver for bound constraints
Multistart and global search solvers for smooth problems with linear, nonlinear, and bound constraints