Simulated annealing is a relatively new technique for solving global optimization problems. The Hide & Seek method is an extension of the basic technique to the continuous domain in an adaptive and efficient manner. It also does not impose conditions such as differentiability or continuity on the function being optimized. This paper describes the Hide & Seek techniqueand appliesittotwo standard mathematical problemsin global optimization.The same problems are also solved using Pure Random Search and a careful comparison of the performance of the two methods is made.Results indicate that the Hide & Seek algorithm is a strong candidate for consideration in solving global optimization methods with a few variables.  
A large number of algorithms introduced in the literature to find the global minimum of a real func...
In the early 1980s, Kirkpatrick et al. [1] and, independently, Černý [2] introduced simulated anneal...
This implementation of simulated annealing was used in "Global Optimization of Statistical Functions...
Hide-and-Seek is a powerful yet simple and easily implemented continuous simulated annealing algorit...
In this paper we are concerned with global optimization, which can be defined as the problem of find...
Abstract Simulated annealing is a popular local search meta-heuristic used to address discrete and, ...
Abstract. Global optimization involves the difficult task of the identification of global extremitie...
Simulated annealing is a global optimization method that distinguishes between different local optim...
In this paper several probabilistic search techniques are developed for global optimization under th...
International audienceFinding the global minimum of a nonconvex optimization problem is a notoriousl...
Many statistical methods rely on numerical optimization to estimate a model\u27s parameters. Unfortu...
AbstractIn this paper, we establish some bounds for the probability that stimulated annealing produc...
Simulated annealing is an established method for global optimization. Perhaps its most salient featu...
AbstractThe development in the area of randomized search heuristics has shown the importance of a ri...
In this talk we consider the problem of finding all the global solutions of a nonlinear optimization...
A large number of algorithms introduced in the literature to find the global minimum of a real func...
In the early 1980s, Kirkpatrick et al. [1] and, independently, Černý [2] introduced simulated anneal...
This implementation of simulated annealing was used in "Global Optimization of Statistical Functions...
Hide-and-Seek is a powerful yet simple and easily implemented continuous simulated annealing algorit...
In this paper we are concerned with global optimization, which can be defined as the problem of find...
Abstract Simulated annealing is a popular local search meta-heuristic used to address discrete and, ...
Abstract. Global optimization involves the difficult task of the identification of global extremitie...
Simulated annealing is a global optimization method that distinguishes between different local optim...
In this paper several probabilistic search techniques are developed for global optimization under th...
International audienceFinding the global minimum of a nonconvex optimization problem is a notoriousl...
Many statistical methods rely on numerical optimization to estimate a model\u27s parameters. Unfortu...
AbstractIn this paper, we establish some bounds for the probability that stimulated annealing produc...
Simulated annealing is an established method for global optimization. Perhaps its most salient featu...
AbstractThe development in the area of randomized search heuristics has shown the importance of a ri...
In this talk we consider the problem of finding all the global solutions of a nonlinear optimization...
A large number of algorithms introduced in the literature to find the global minimum of a real func...
In the early 1980s, Kirkpatrick et al. [1] and, independently, Černý [2] introduced simulated anneal...
This implementation of simulated annealing was used in "Global Optimization of Statistical Functions...