Global Optimization Toolbox
Product Description
- Key Features
- Defining, Solving, and Assessing Optimization Problems
- Global Search and Multistart Solvers
- Genetic Algorithm Solver
- Multiobjective Genetic Algorithm Solver
- Pattern Search Solver
- Simulated Annealing Solver
- Solving Optimization Problems Using Parallel Computing
Solving Optimization Problems Using Parallel Computing
You can use Global Optimization Toolbox in conjunction with Parallel Computing Toolbox to solve problems that benefit from parallel computation. By using built-in parallel computing capabilities or defining a custom parallel computing implementation of your optimization problem, you decrease time to solution.
Built-in support for parallel computing accelerates the objective and constraint function evaluation in genetic algorithm, multiobjective genetic algorithm, and pattern search solvers. You can accelerate the multistart solver by distributing the multiple local solver calls across multiple MATLAB workers or by enabling the parallel gradient estimation in the local solvers.
Optimizing Shift Schedule to Maximize Fuel Economy 5:33 new
Optimize 20 parameters in a shift schedule to maximize fuel economy for a dual-clutch transmission. Global optimization algorithms and parallel computing are used to accelerate the optimization.
A custom parallel computing implementation involves explicitly defining the optimization problem to use parallel computing functionality. You can define either your objective function or constraint function to use parallel computing, letting you decrease the time required to evaluate the objective or constraint.
Nonlinear Regression with the Multistart Solver 4:16
The best-fit parameters for a semi-empirical model are found using parallel computing.
