de Groot, C.; Würtz, D.; Hanf, M.; Hoffmann, K. H.; Peikert, R.; Koller, Th. Stochastic optimization – Efficient algorithms to solve complex problems. (English) Zbl 0792.90067 Kall, Peter (ed.), System modelling and optimization. Proceedings of the 15th IFIP conference, Zurich, Switzerland, September 2-6, 1991. Berlin: Springer-Verlag. Lect. Notes Control Inf. Sci. 180, 546-555 (1992). The aim of the paper is twofold: First we want to introduce briefly three of the lately developed stochastic optimization algorithms. Two of them are inspired by physics: simulated annealing and Langevin equation formalism, the third is termed evolution strategy making its biological descendence obvious. In a second step we apply these optimization methods to standard problems, compare them, and discuss the results.For the entire collection see [Zbl 0778.00028]. MSC: 90C30 Nonlinear programming 90-08 Computational methods for problems pertaining to operations research and mathematical programming 90C27 Combinatorial optimization Keywords:stochastic optimization; simulated annealing; Langevin equation formalism; evolution strategy PDFBibTeX XMLCite \textit{C. de Groot} et al., Lect. Notes Control Inf. Sci. 180, 546--555 (1992; Zbl 0792.90067)