Abstract
The paper describes the main aspects of global optimization on the base of the evolutionary optimization algorithms. The possibilities of MATLAB Global Optimization Toolbox have been discussed. It is shown, that the corresponding program codes can be used for solving optimization problems, for study of the evolutionary algorithms, for their modification and hybridization.
Highlights
The first steps in the studying of optimization algorithms are connected with application of the gradient-based optimization algorithms
Gradient-based optimization algorithm is an algorithm to solve problems of the form min f (x) (where f (x) is the objective x Rn function) with the search directions defined by the gradient of the function at the current point
If the objective function has many local minima and the search starts from the initial point close to one such local minimum, the algorithm will most probably find the local minimum closest to the initial point
Summary
The first steps in the studying of optimization algorithms are connected with application of the gradient-based optimization algorithms. Gradient-based optimization algorithm is an algorithm to solve problems of the form min f (x) (where f (x) is the objective x Rn function) with the search directions defined by the gradient of the function at the current point. The most known gradient-based optimization algorithms are the gradient descent algorithm and the conjugate gradient algorithm [1]. To find the global minimum for such objective functions it is necessary to locate the global minimum (but it in real problems isn't always possible) or to execute repeatedly the gradient-based optimization algorithm with the different randomly chosen initial approximations of the solution, that demands the huge time expenditures. New optimization algorithms such as the evolutionary search-based algorithms have been created
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