Vladimir Sedenka

Bio: Vladimir Sedenka is an academic researcher from Brno University of Technology. The author has contributed to research in topics: Time domain & Decoupling capacitor. The author has an hindex of 3, co-authored 8 publications receiving 38 citations.

Critical Comparison of Multi-objective Optimization Methods: Genetic Algorithms versus Swarm Intelligence

Abstract: The paper deals with efficiency comparison of two global evolutionary optimization methods implemented in MATLAB. Attention is turned to an elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and a novel multi-objective Particle Swarm Optimization (PSO). The performance of optimizers is compared on three different test functions and on a cavity resonator synthesis. The microwave resonator is modeled using the Finite Element Method (FEM). The hit rate and the quality of the Pareto front distribution are classified.

PCB Decoupling Optimization With Variable Number of Capacitors

Abstract: The decoupling of modern printed circuit boards introduces a very complex task. Powerful stochastic optimizers are usually used to determine values and positions of decoupling capacitors on the board. The number of capacitors used has to be determined a priori by the user which brings problems with convergence of the optimization process or can lead to a waste of resources when the noises are to be attenuated to a certain level. In this paper, an approach based on the combination of time-domain contour integral method and optimization with variable number of dimensions is introduced. The optimizer works with models having variable dimensions and searches for the optimal one. The approach is tested on two example power circuit boards with various noise attenuation limits and constraints on capacitor positions and values.

AToM: a versatile MATLAB tool for antenna synthesis

Abstract: The Antenna Toolbox For MATLAB (AToM), originally an in-house academic tool, has been transformed into a complete MATLAB toolbox, capable of modeling, discretizing and calculating arbitrarily shaped planar radiators while analysing the results. All tasks can be performed directly in MATLAB. The majority of the code allows its direct modification. AToM supports the latest features, used predominantly in the realm of electrically small antennas, namely modal decompositions, evaluation of fundamental bounds, and other techniques based on the source concept.

Solution of an inverse scattering problem using optimization with a variable number of dimensions

Abstract: The inverse scattering problem applies in many areas of engineering such as biomedicine, civil engineering, electromagnetic compatibility or geophysics. In this paper, one-dimensional inverse scattering problem is formulated as an optimization task with variable number of dimensions. Goal is to reconstruct material properties of a layered medium from its reflection coefficient. Then, this problem is solved using PSO-VND algorithm. Its solution is compared with solutions by conventional approaches: PSO, GA and DE optimizers. Results show that VND formulation significantly reduces computational time.

On modeling of excitation ports in the time-domain contour-integral method

Abstract: This contribution reports on modeling of excitation ports in the time-domain contour integral method. In this respect, two modeling strategies are presented. The first approach is very general and allows for the incorporation of rather complex excitation mechanisms including the vertical-probe and microstrip-line feeds. The second way is computationally more efficient but its application is limited to special cases. A numerical example that validates the feeding model is given.

GALE: Geometric Active Learning for Search-Based Software Engineering

Abstract: Multi-objective evolutionary algorithms (MOEAs) help software engineers find novel solutions to complex problems. When automatic tools explore too many options, they are slow to use and hard to comprehend. GALE is a near-linear time MOEA that builds a piecewise approximation to the surface of best solutions along the Pareto frontier. For each piece, GALE mutates solutions towards the better end. In numerous case studies, GALE finds comparable solutions to standard methods (NSGA-II, SPEA2) using far fewer evaluations (e.g. 20 evaluations, not 1,000). GALE is recommended when a model is expensive to evaluate, or when some audience needs to browse and understand how an MOEA has made its conclusions.

A Novel Multi-Objective Self-Organizing Migrating Algorithm

Abstract: In the paper, a novel stochastic Multi-Objective Self-Organizing Migrating Algorithm (MOSOMA) is introduced. For the search of optima, MOSOMA employs a migration technique used in a single-objective Self Organizing Migrating Algorithm (SOMA). In order to obtain a uniform distribution of Pareto optimal solutions, a novel technique considering Euclidian distances among solutions is introduced. MOSOMA performance was tested on benchmark problems and selected electromagnetic structures. MOSOMA performance was compared with the performance of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2). MOSOMA excels in the uniform distribution of solutions and their completeness.