Lipschitz global optimization appears in many practical problems: decision making, optimal control, stability problems, finding the minimal root problems, etc. In many engineering applications the objective function is a “black-box”, multiextremal, non-differentiable and hard to evaluate. Another common property of the function to be optimized very often is the Lipschitz condition. In this talk, the Lipschitz global optimization problem is considered and several nature-inspired and Lipschitz global optimization algorithms are briefly described and compared with respect to the number of evaluations of the objective function.
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