Stochastic global optimization methods. part 1: clustering methods
Mathematical Programming: Series A and B
Stochastic global optimization methods. part 11: multi level methods
Mathematical Programming: Series A and B
On the convergence of stationary distributions in simulated annealing algorithms
Information Processing Letters
New computer methods for global optimization
New computer methods for global optimization
Global optimization and simulated annealing
Mathematical Programming: Series A and B
Recent advances in global optimization
Recent advances in global optimization
Topographical global optimization
Recent advances in global optimization
Numerical Solution of Optimal Control Problems with Discrete-Valued System Parameters
Journal of Global Optimization
Population set-based global optimization algorithms: some modifications and numerical studies
Computers and Operations Research
Efficient hybrid methods for global continuous optimization based on simulated annealing
Computers and Operations Research
Global optimization models in data networks: a case study
Computers and Operations Research
Financial Optimization Models in Data Networks*
Journal of Global Optimization
Optimizing an Empirical Scoring Function for Transmembrane Protein Structure Determination
INFORMS Journal on Computing
Efficient hybrid methods for global continuous optimization based on simulated annealing
Computers and Operations Research
Global optimization models in data networks: a case study
Computers and Operations Research
A simulated annealing driven multi-start algorithm for bound constrained global optimization
Journal of Computational and Applied Mathematics
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An aspiration based simulated annealing algorithm for continuousvariables has been proposed. The new algorithm is similar to the one givenby Dekkers and Aarts (1991) except that a kind of memory is introduced intothe procedure with a self-regulatory mechanism. The algorithm has beenapplied to a set of standard global optimization problems and a number ofmore difficult, complex, practical problems and its performance comparedwith that of the algorithm of Dekkers and Aarts (1991). The new algorithmappears to offer a useful alternative to some of the currently availablestochastic algorithms for global optimization.