GLOBAL OPTIMIZATION VERY FAST SIMULATED ANNEALING INVERSION FOR THE INTERPRETATION OF GROUNDWATER POTENTIAL

Very Fast Simulated Re-Annealing

Simulated Annealing and Global Optimization

Nelder-Mead (when you don’t know ∇f ) and steepest descent/conjugate gradient (when you do). Both of these methods are based on attempting to generate a sequence of positions xk with monotonically decreasing f(xk) in the hopes that the xk → x∗, the global minimum for f . If f is a convex function (this happens surprisingly often), and has only one local minimum, these methods are exactly the ri...

Global optimization and simulated annealing

In this paper we are concerned with global optimization, which can be defined as the problem of finding points on a bounded subset of N" in which some real valued function f assumes its optimal (maximal or minimal) value. We present a stochastic approach which is based on the simulated annealing algorithm. The approach closely follows the formulation of the simulated annealing algorithm as orig...

Global optimization and simulated annealing

In this paper we are concerned with global optimization, which can be defined as the problem of finding points on a bounded subset of IRn in which some real valued functionf assumes its optimal (i.e. maximal or minimal) value. We present a stochastic approach which is based on the simulated annealing algorithm. The approach closely follows the formulation of the simulated annealing algorithm as...

Simulated annealing for constrained global optimization

Hide-and-Seek is a powerful yet simple and easily implemented continuous simulated annealing algorithm for finding the maximum of a continuous function over an arbitrary closed, bounded and full-dimensional body. The function may be nondifferentiable and the feasible region may be noneonvex or even disconnected. The algorithm begins with any feasible interior point. In each iteration it generat...