Global Optimization with Exploration/Selection Algorithms and Simulated Annealing

Parallel Simulated Annealing Algorithms in Global Optimization

Global optimization involves the difficult task of the identification of global extremities of mathematical functions. Such problems are often encountered in practice in various fields, e.g., molecular biology, physics, industrial chemistry. In this work, we develop five different parallel Simulated Annealing (SA) algorithms and compare them on an extensive test bed used previously for the asse...

Global Optimization with Exploration/Selection Algorithms and Simulated 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...