Global Optimization Toolbox
Global Optimization Toolbox offers tools for finding global solutions to problems that may have several local maxima or minima. Its solvers include surrogate optimization, pattern search, genetic algorithms, particle swarm, simulated annealing, multistart, and global search methods. These solvers are suited for optimization tasks where the objective or constraint functions may be continuous, discontinuous, stochastic, non-differentiable, or based on simulations and black-box models. For multiobjective problems, genetic algorithm and pattern search solvers can be used to generate a Pareto front.
Solver performance can be enhanced by tuning configuration options and, when applicable, customizing creation, update, and search functions. Genetic algorithm and simulated annealing solvers also support custom data types, enabling you to model problems that cannot be easily represented with standard formats. Additionally, the hybrid function feature allows you to refine solutions by applying a secondary solver starting from the result of the initial solver.
Define and Solve Optimization Problems
Define your optimization problem, apply a solver, and set options for algorithm behavior, tolerances, stopping criteria, visualizations, and customizations.
GlobalSearch and MultiStart
Apply gradient-based solvers to find local minima from multiple starting points in search of global minima. Other local or global minima are returned. Solve unconstrained and constrained problems that are smooth.
Surrogate Optimization
Search for global minima on problems with time-consuming objective functions, which can be nonsmooth. The solver builds an approximation to the function that can be quickly evaluated and minimized.
Pattern Search
Start from the current point and add a set of vectors to get new trial points. Evaluate the objective function on the trial points and use that information to update the current point. Repeat until the current point is an optimum.
Genetic Algorithm
Search for global minima by mimicking the principles of biological evolution, repeatedly modifying a population of individual points using rules modeled on gene combinations in biological reproduction.
Particle Swarm
Search for global minima using an algorithm inspired by the behavior of insects swarming. Each particle moves with a velocity and direction influenced by the best location it has found so far and the best location the swarm has found.
Simulated Annealing
Search for global minima with a probabilistic search algorithm that mimics the physical process of annealing, in which a material is heated and then the temperature is slowly lowered to decrease defects, thus minimizing the system energy.
Multiobjective Optimization
Identify the Pareto front—the set of nondominated solutions—for problems with multiple objectives and bound, linear, or nonlinear constraints. Use either the pattern search or genetic algorithm solvers.
