Deterministic annealing and relaxation labeling algorithms for classification and matching are presented and discussed. A new approach—self annealing—is introduced to bring deterministic annealing and relaxation labeling into accord. Self annealing results in an emergent linear schedule for winner-take-all and linear assignment problems. Self annihilation, a generalization of self annealing is ...

undoped and mn-doped zno nanoparticles were successfully prepared by the hydrothermal method with different annealing temperature conditions. structural, chemical and optical properties of the samples were studied by x-ray diffraction (xrd), field emission scanning electron microscopy (fesem), uv-vis spectrophotometry and fourier transform infrared (ft-ir) spectroscopy. the phase purity was con...

Deterministic annealing and relaxation labeling algorithms for classiication and matching are presented and discussed. A new approach |self annealing|is introduced to bring deterministic annealing and relaxation labeling into accord. Self annealing results in an emergent linear schedule for winner-take-all and assignment problems. Also, the relaxation labeling algorithm can be seen as an approx...

In this work, the formation of austenite in intercritical interval under continuous heating and isothermal holding ductile iron with various chemistries was investigated using high-resolution dilatometry quantitative metallographic analysis. The study conducted fully ferritic pearlitic matrices as initial microstructures. Subsequently, a mathematical model based on Avrami's equation, that descr...

As multiobjective optimization problems have many solutions, evolutionary algorithms have been widely used for complex multiobjective problems instead of simulated annealing. However, simulated annealing also has favorable characteristics in the multimodal search. We developed several simulated annealing schemes for the multiobjective optimization based on this fact. Simulated annealing and evo...

We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference. Variational inference is a deterministic approach to approximate posterior inference in Bayesian models in which a typically non-convex objective function is locally optimized over the parameters of the approximating distribution. We investigate an annealing method for optimiz...

Capabilities of enhanced simulated-annealing-based algorithms in solving process planning problems in reconfigurable manufacturing are investigated. The algorithms are enhanced by combining variants of the simulated annealing technique with other algorithm concepts such as (i) knowledge exploitation and (ii) parallelism. Four configurations of simulated annealing algorithms are devised and enga...