We present a parallel computational environment used to determine conductivity properties of human head tissues when the effects of skull inhomogeneities are modeled. The environment employs a parallel simulated annealing algorithm to overcome poor convergence rates of the simplex method for larger numbers of head tissues required for accurate modeling of electromagnetic dynamics of brain funct...

In this paper, a multi-objective project scheduling problem is addressed. This problem considers two conflicting, priority optimization objectives for project managers. One of these objectives is to minimize the project makespan. The other objective is to assign the most effective set of human resources to each project activity. To solve the problem, a multi-objective hybrid search and optimiza...

This paper presents a method based on simulated annealing to learn reactive behaviors. This work is related with multi-agent systems. It is a first step towards automatic generation of sensorimotor control architectures for completing complex cooperative tasks with simple reactive mobile robots. The controller of the agents is a neural network and we use a simulated annealing techniques to lear...

The aim of this paper is to examine two new simulated annealing approaches for Uncapacitated Facility Location (UFL) problems with some useful comparisons with the latest genetic algorithm approach (Jaramillo et al, 2002) to the problem. As it is mentioned in various publications, the simulated annealing approach may be very successful in terms of quality of the solution but not so impressive w...

An MCM's increased throughput and dense circuitry can easily result in failure if the board contains \hot spots". Therefore, an accurate thermal model of an MCM was needed in the development of a new placement algorithm designed to consider both total net length and heat constraints. This algorithm uses a combination of simulated evolution and simulated annealing in an iterative approach. The t...

SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller's scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neura...