Boltzmann evolutionary algorithms and their embedded selection mechanisms are traditionally employed to prolong search. After a brief introduction, a precursor called simulated annealing is outlined. A prominent type of Boltzmann evolutionary algorithm called parallel recombinative simulated annealing is then covered in depth. A proof of global convergence for this type of algorithm is illustra...

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...

We propose a variant of the Simulated Annealing method for optimization in the multivariate analysis of diierentiable functions. The method uses the Hybrid Monte Carlo algorithm for the proposal of new conngurations. We show how this choice can improve the performance of simulated annealing methods by allowing much faster annealing schedules.

Adaptive simulated annealing (ASA) is a global optimization algorithm based on an associated proof that the parameter space can be sampled much more efficiently than by using other previous simulated annealing algorithms. The author’s ASA code has been publicly available for over two years. During this time the author has volunteered to help people via e-mail, and the feedback obtained has been...

Make-to-order is a production strategy in which manufacturing starts only after a customer's order is received; in other words, it is a pull-type supply chain operation since manufacturing is carried out as soon as the demand is confirmed. This paper studies the order acceptance problem with weighted tardiness penalties in permutation flow shop scheduling with MTO production strategy, the objec...

Transaction cost function minimization is an important problem in finance. In this essay, we develop and apply a simulated annealing and smoothing method to this particular problem. We illustrate that this method is an improvement over using the trust-region method or simulated annealing algorithm alone. We will provide examples in different dimensions and using different parameter settings to ...

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 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...

We here present the details of the numerical realization of the recently advanced algorithm developed to identify the fragmentation in heavy ion reactions. This new algorithm is based on the Simulated Annealing method and is dubbed as Simulated Annealing Clusterization Algorithm [SACA]. We discuss the different parameters used in the Simulated Annealing method and present an economical set of t...

The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization pro...