We present an extension of continuous domain Simulated Annealing. Our algorithm employs a globally reaching candidate generator, adaptive stochastic acceptance probabilities, and converges in probability to the optimal value. An application to simulation-optimization problems with asymptotically diminishing errors is presented. Numerical results on a noisy protein-folding problem are included.

This paper presents an adaptive procedure for the linear and non-linear separation of signals with non-uniform, symmetrical probability distributions, based on both simulated annealing and competitive learning methods by means of a neural network, considering the properties of the vectorial spaces of signals, and using a multiple linearization in the mixture space.

The paper presents a hybrid adaptation method that combines a knowledge-based approach with reinforcement learning and a simulated annealing technique, and is applied in the navigation of an educational mobile robot. The experimental results of simulations showed a good behaviour of the robot when doing an adaptive navigation in a dynamic environment by using the proposed hybrid method.

An algorithm is developed to statistically find the best global fit of a nonlinear non-convex cost-function over a D-dimensional space. It is argued that this algorithm permits an annealing schedule for ''temperature'' T decreasing exponentially in annealing-time k, T = T 0 exp(−ck 1/D). The introduction of re-annealing also permits adaptation to changing sensitivities in the multi-dimensional ...

We propose a new stochastic algorithm (generalized simulated annealing) for computationally finding the global minimum of a given (not necessarily convex) energy/cost function defined in a continuous D-dimensional space. This algorithm recovers, as particular cases, the so called classical (“Boltzmann machine”) and fast (“Cauchy machine”) simulated annealings, and can be quicker than both. Key-...

We applied simulated annealing algorithm to nurse scheduling problem. For time complexity problem of simulated annealing, we suggested an efficient transition rule using cost matrix for simulated annealing. The experimental results showed that the suggested method generated a nurse scheduling faster in time and better in quality compared to traditional simulated annealing.

Simulated annealing is known to be an efficient method for combinatorial optimization problems. Its usage for real-life problems has been limited by the long execution time. This report presents a new approach to asynchronous simulated annealing for parallel thread oriented multiprocessor operating systems. Experimental results of the 100to 1000-city traveling salesman problems on the two-proce...

n different nodes of the network. The two algorithms have been applied to the job shop scheduling problem (JSS) and the traveling salesman problem (TSP). Both algorithms showed very good performance in terms of solution time and solution quality. The rest of the paper is organized in the following fashion. Section II describes the simulated annealing technique. Section III presents the cluster ...

Received on:2/6/2009 Accepted on:1/4/2010 Abstract Simulated annealing (SA) has been considered as a good tool for search and optimization problems which represent the abstraction of obtaining the crystalline structure through a physical process. This algorithm works sequentially that the current state will produce only one next state. That will make the search to be slower and the important dr...