A novel approach for thin film thickness and optical constant extraction from spectral reflectance data is presented here. This methodology combines the global minimization abilities of Adaptive Simulated Annealing with the high computational efficiency of Neural Networks to solve complex characterization problems in real time. The optical constants of many thin films such as Polysilicon are a ...

We discuss and implement a new strategy for spatial cluster detection. A test statistic based on the likelihood ratio is used, as formulated by Kulldorff and Nagarwalla. Differently from these authors, our test is not restricted to the detection of clusters with fixed shape, such as rectangular or circular shape, but it looks for connected clusters with arbitrary geometry. This could be advanta...

Template learning has potential application in several areas of Cellular Neural Network research, including texture recognition, pattern detection and so on. In this letter, a recently-developed algorithm called Adaptive Simulated Annealing is investigated for learning CNN templates, as a superior alternative to the Genetic Algorithm. key words: adaptive simulated annealing, cellular neural net...

Topology optimization (TO) of engineering products is an important design task to maximize performance and efficiency, which can be divided into two main categories gradient-based non-gradient-based methods. In recent years, significant attention has been brought the methods, mainly because they do not demand access derivatives objective functions. This property makes them well compatible struc...

Simulated annealing (SA) algorithms can be modeled as time-inhomogeneous Markov chains. Much work on the convergence rate of simulated annealing algorithms has been well-studied. In this paper, we propose an adiabatic framework for studying simulated annealing algorithm behavior. Specifically, we focus on the problem of simulated annealing algorithms that start from an initial temperature T0 an...

This paper presents a new simulated annealing algorithm to solve constrained multi-global optimization problems. To compute all global solutions in a sequential manner, we combine the function stretching technique with the adaptive simulated annealing variant. Constraint-handling is carried out through a nondifferentiable penalty function. To benchmark our penalty stretched simulated annealing ...

It was Kirkpatrick et al. who first proposed simulated annealing, SA, as a method for solving combinatorial optimization problems[1]. It is reported that SA is very useful for several types of combinatorial optimization problems[2]. The advantages and the disadvantages of SA are well summarized in [3]. The most remarkable disadvantages are that it needs a lot of time to find the optimum solutio...

In this paper, a Temperature Parallel Simulated Annealing with Adaptive Neighborhood (TPSA/AN) for continuous optimization problems is introduced. TPSA/AN is based on the temperature parallel simulated annealing (TPSA), which is suitable for parallel processing, and the SA that Corana developed for continuous optimization problems. The moves in TPSA/AN are adjusted to have equal acceptance rate...

In this paper, we propose: (a) a restart schedule for an adaptive simulated annealer, and (b) parallel simulated annealing, with an adaptive and parameter-free annealing schedule. The foundation of our approach is the Modified Lam annealing schedule, which adaptively controls the temperature parameter to track a theoretically ideal rate of acceptance of neighboring states. A sequential implemen...