Neighbourhood structure and size are important parameters in local search algorithms. This is also true for generalised local search algorithms like simulated annealing. It has been shown that the performance of simulated annealing can be improved by adopting a suitable neighbourhood size. However, previous studies usually assumed that the neighbourhood size was xed during search. This paper pr...

Article history: Received: 13.2.2015. Received in revised form: 24.3.2015. Accepted: 27.3.2015. In structural design optimization method, numerical techniques are increasingly used. In typical structural optimization problems there may be many locally minimum configurations. For that reason, the application of a global method, which may escape from the locally minimum points, remains essential....

In this paper, we optimize the size of aperture and number of sensors for Wideband Covariance Matrix Sparse representation (W-CMSR) method for Wideband Direction of Arrival estimation using Simulated Annealing. The performance of W-CMSR is obtained with initial number of sensors and aperture size. Then, we find the performance of W-CMSR with optimized number of sensors and aperture size using s...

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 we formulate the combinatorial auction brokering problem as a Set Packing Problem (SPP) and apply a simulated annealing heuristic to solve SPP. Experimental results are compared with a recent heuristic algorithm and also with the CPLEX Integer Programming solver where we found that the simulated annealing approach outperforms the previous heuristic method and CPLEX obtaining resul...

We present a method to speed up the computation of a high-quality data-dependent triangulation approximating an image using simulated annealing by probability distributions guided by local approximation error and its variance. The triangulation encodes the image, yielding compression rates comparable to or even superior to JPEG and JPEG2000 compression. The specific contributions of our paper a...

Various approaches of incorporating chaos into artificial neural networks have recently been proposed, and used successfully to solve combinatorial optimisation problems. This paper investigates three such approaches: 1) Chen & Aihara's transiently chaotic neural network with chaotic simulated annealing, which has a gradually decaying negative selfcoupling term; 2) Wang & Smith's chaotic simula...

This paper attempts to determine the mechanisms governing the grain growth process that occurs during lamination annealing of a cold-rolled, motor-lamination steel. A new simulation approach linking a Monte Carlo model, with EBSD scans used as input, has been employed to incorporate the effects of crystallographic texture on the simulated grain growth process. The results from the texture analy...

Simulated annealing is a multivariable optimization technique based on the Monte Carlo method used in statistical mechanical studies of condensed systems and follows by drawing an analogy between energy minimization in physical systems and costs minimization in design applications. In this paper, simulated annealing is introduced and reviewed. The utility of the method for optimization of chemi...

A Simulated Annealing method is presented for the solution of nonlinear time series estimation problems, by maximization of the a Posteriori Likelihood function. Homogeneous temperature annealing is proposed for smoothing problems and inhomogeneous temperature annealing for filtering problems. Both methods of annealing guarantee convergence to the Maximum A Posteriori Likelihood (MAP) estimate....