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Abstract: In this paper we show that the unconstrained continuous global minimization problem cannot be solved by any algorithm, so we consider the box constrained continuous global minimization problem. We present a definition of filled function for the problem. Moreover, a new class of filled functions are constructed, which contains only one parameter. A randomized algorithm is designed to solve the box constrained continuous global minimization problem based on the filled function. Numerical experiments are presented to show the practicability of the algorithm. [Copyright &y& Elsevier]

Abstract: We propose a derivative-free implementation of Dekkers and Aarts’ continuous simulated annealing (SA). Essential in DFSA is the ’random direction’ local technique that we introduce. We study the convergence properties of the resulting algorithm and test its performance on a set of 50 problems. Numerical results are presented which show the robustness of the algorithm. Numerical comparisons with SA, two derivative-free simulated annealing algorithms and three population-based global optimization algorithms show that the derivative-free SA, DFSA, offers a reasonable alternative to some recent global optimization algorithms, especially for problems requiring a ‘direct search’ type algorithm. [Copyright &y& Elsevier]