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.
Global search and multistart solvers for finding single or multiple global optima
Genetic algorithm for linear, nonlinear, bound, and integer constraints with customization by defining parent selection, crossover, and mutation functions
Multiobjective genetic algorithm with Pareto-front identification, including linear, nonlinear, and bound constraints
Pattern search solver for linear, nonlinear, and bound constraints with customization by defining polling, searching, and other functions
Simulated annealing solver for bound constraints, with options for defining annealing process, temperature schedule, and acceptance criteria
Particle swarm solver for bound constraints with options for defining the initial particles and swarm behavior