Global Optimization Toolbox

Global Search and Multistart Solvers

The global search and multistart solvers use gradient-based methods to return local and global minima. Both solvers start a local solver (in Optimization Toolbox) from multiple starting points and store local and global solutions found during the search process.

The global search solver:

  • Uses a scatter-search algorithm to generate multiple starting points
  • Filters nonpromising start points based upon objective and constraint function values and local minima already found
  • Runs a constrained nonlinear optimization solver to search for a local minimum from the remaining start points

The multistart solver uses either uniformly distributed start points within predefined bounds or user-defined start points to find multiple local minima, including a single global minimum if one exists. The multistart solver runs the local solver from all starting points and can be run in serial or in parallel (using Parallel Computing Toolbox). The multistart solver also provides flexibility in choosing different local nonlinear solvers. The available local solvers include unconstrained nonlinear, constrained nonlinear, nonlinear least-squares, and nonlinear least-squares curve fitting.

Using Global Search for Optimization Problems 3:57
Find local and global minima of the peaks function.

Using MultiStart for Optimization Problems 4:16
Find the best-fit parameters for an exponential model.

Next: Genetic Algorithm Solver

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