ABSTRACT
The Mathematica software has a collection of commands which make exact-numeric optimization to solve linear-nonlinear and unconstrained- constrained problems. In this respect, NMinimize and NMaximize commands are used in numeric global optimization methods while Minimize and Maximize commands are only appropriate for exact global optimization. The Random Search algorithm implemented by Mathematica has a stochastic approach. In the working process, the algorithm composes population, including random starting points, and then the algorithm evaluates the convergence behavior of the starting points to the local minimum utilizing the FindMinimum local search method. The Simulated Annealing algorithm implemented by Mathematica is a stochastic approach having a working process based on the physical annealing procedure of solids. Differential evolution is one of the most common stochastic search algorithms in the optimization and solution of complicated and challenging design problems. Nelder Mead algorithm or Simplex is one of the derivative-free optimization methods among other traditional local search algorithms.
