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Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm. (English) Zbl 0632.65075
From authors’ summary: A new global optimization algorithm for functions of continuous variables is presented, derived from the “simulated annealing” algorithm recently introduced in combinatorial optimization. The algorithm is essentially an iterative random search procedure with adaptive moves along the coordinate directions. It permits uphill moves under the control of a probabilistic criterion, thus tending to avoid the first local minima encountered. The new method proved to be more reliable than the others (the Nelder-Mead method and the adaptive random search method of the reviewer), being always able to find the optimum, or at least a point very close to it. It is quite costly in terms of function evaluations, but its cost can be predicted in advance, depending only slightly on the starting point.
Reviewer: J.Rohn

65K05 Numerical mathematical programming methods
90C15 Stochastic programming
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