Different successful heuristic approaches have been proposed for solving combinatorial optimization problems. Commonly, each of them is specialized to serve a different purpose or address specific difficulties. However, most combinatorial problems that model real world applications have a priori well known measurable properties. Embedded machine learning methods may aid towards the recognition ...

The purpose of image fusion is to create a perceptually enhanced image from a set of multi-focus or multi-sensors images. In the methods we are about to describe we do not a priori know the ground truth image: these are blind fusion methods. There are mainly two groups of fusion methods depending on the signal domain they are applied: spatial domain methods and transform domain methods. The Dis...

Optimization problems with “expensive” and “black box” objectives are difficult to tackle. Some experience is accumulated in single-objective global optimization of those problems using algorithms based on statistical models of objective functions. The generalization of this approach to the multi-objective optimization is discussed.

There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very ...

Electrosynthesis process has been used for preparation of zinc sulfide nanoparticles. Zinc sulfide nanoparticles in different size and shapes were electrodeposited by electrolysis of zinc plate as anode in sodium sulfide solution. Effects of several reaction variables, such as electrolysis voltage, sulfide ion concentration as reactant, stirring rate of electrolyte solution and temperature on p...

In this paper, we investigate the convergence of a sequence of minimizing trajectories in infinite horizon optimization problems. The convergence is considered in the sense of ideals and their particular case called the statistical convergence. The optimality is defined as a total cost over the infinite horizon.