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Computational experience with generalized simulated annealing over continuous variables. (English) Zbl 0684.65061
This paper reports computational results obtained using the generalized simulated annealing method (GSA), introduced by I. O. Bohachevsky, M. E. Johnson and M. L. Stein [Technometrics 28, 209–217 (1986; Zbl 0609.65045)] which uses the current function value to control the random process, on a set of seven standard global optimization test problems with continuous variables, and compares to those obtained using some well-known competing stochastic methods.
It seems that although GSA may be competitive with these methods when solving these test problems of the size, its performance on functions with continuous variables may be worse than that on functions with discrete variables. It is less attractive that application of GSA to functions of continuous variables increasing the complexity of the process, and the sensitivity of its performance to the parameters, such as S (step size), necessitates user interaction to “tune” the algorithm.
Reviewer: Pingqi Pan

MSC:
65K05 Numerical mathematical programming methods
90C31 Sensitivity, stability, parametric optimization
90C15 Stochastic programming
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