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GeMMa Activity Report 2016-2022
Tamara Golob, 2023, proceedings

Abstract: The primary aim of this publication is to support the dissemination of the research achievements of GeMMA Laboratory at the Faculty of Electrical Engineering and Computer Science of the University of Maribor. It follows the first survey published in 2016, and therefore, the actual book concentrates on the research results since then. The previous book, covering a period of 17 years, presented 35 R&D projects, while the new review of activities over the last 7 years covers as many as 58 projects. This growth is a good cue to introduce the secondary, equally important aim of the book. Namely, we would like to leave the track to our successors to stimulate them for even better research results, to show them, what is possible to achieve in 22 years starting from scratch with the will, hard work, orientation towards the applications, devotion to the research work, and the strong team spirit.
Keywords: research & development projects, geospatial modelling, multimedia, artificial intellingence, algorithms, industrial cooperation, international cooperation
Published in DKUM: 09.03.2023; Views: 445; Downloads: 50
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Optimization methods for a direct current electric motor design : magistrsko delo
Vid Černec, 2022, master's thesis

Abstract: In the modern world, the need for planning in advance has become increasingly important. The companies want to know the cost, the dimensions of the elements, and the properties of the final product in advance. Therefore, we can find teams working on the analysis and development of new and different products in larger companies. More experienced workers can determine the given results and see what would be acceptable by simply looking at the situation. Although, the help of technology makes the process easier and faster, and even provides the same result. When creating a specific model, we must focus on the equations describing our result. Then we begin with optimization, which means we define the variables, which will be our subject of research in the project, and define some fixed parameters until the desired goals are reached.
Keywords: design, DC electric motor, optimization, optimization algorithms, genetic algorithms
Published in DKUM: 21.10.2022; Views: 556; Downloads: 37
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5.
Distance-based Invariants and Measures in Graphs
Aleksander Kelenc, 2019, doctoral dissertation

Abstract: This doctoral dissertation is concerned with aspects on distance related topics in graphs. We study three main topics, namely a recently introduced measure called the Hausdorff distance of graphs and two new graph invariants - the edge metric dimension and the mixed metric dimension of graphs. All three topics are part of the metric graph theory since they are tightly connected with the basic concept of distance between two vertices of a graph. The Hausdorff distance is a relatively new measure of the similarity of graphs. The notion of the Hausdorff distance considers a special kind of common subgraph of the compared graphs and depends on the structural properties outside of the common subgraph. We study the Hausdorff distance between certain families of graphs that often appear in chemical graph theory. Next to a few results for general graphs, we determine formulae for the distance between paths and cycles. Previously, there was no known efficient algorithm for the problem of determining the Hausdorff distance between two trees, and in this dissertation we present a polynomial-time algorithm for it. The algorithm is recursive and it utilizes the divide and conquer technique. As a subtask it also uses a procedure that is based on the well-known graph algorithm for finding a maximum bipartite matching. The edge metric dimension is a graph invariant that deals with distinguishing the edges of a graph. Let $G=(V(G),E(G))$ be a connected graph, let $w \in V(G)$ be a vertex, and let $e=uv \in E(G)$ be an edge. The distance between the vertex $w$ and the edge $e$ is given by $d_G(e,w)=\min\{d_G(u,w),d_G(v,w)\}$. A vertex $w \in V(G)$ distinguishes two edges $e_1,e_2 \in E(G)$ if $d_G(w,e_1) \ne d_G(w,e_2)$. A set $S$ of vertices in a connected graph $G$ is an edge metric generator of $G$ if every two distinct edges of $G$ are distinguished by some vertex of $S$. The smallest cardinality of an edge metric generator of $G$ is called the edge metric dimension and is denoted by $dim_e(G)$. The concept of the edge metric dimension is new. We study its mathematical properties. We make a comparison between the edge metric dimension and the standard metric dimension of graphs while presenting some realization results concerning the two. We prove that computing the edge metric dimension of connected graphs is NP-hard and give some approximation results. Moreover, we present bounds and closed formulae for the edge metric dimension of several classes of graphs. The mixed metric dimension is a graph invariant similar to the edge metric dimension that deals with distinguishing the elements (vertices and edges) of a graph. A vertex $w \in V(G)$ distinguishes two elements of a graph $x,y \in E(G)\cup V(G)$ if $d_G(w,x) \ne d_G(w,y)$. A set $S$ of vertices in a connected graph $G$ is a mixed metric generator of $G$ if every two elements $x,y \in E(G) \cup V(G)$ of $G$, where $x \neq y$, are distinguished by some vertex of $S$. The smallest cardinality of a mixed metric generator of $G$ is called the mixed metric dimension and is denoted by $dim_m(G)$. In this dissertation, we consider the structure of mixed metric generators and characterize graphs for which the mixed metric dimension equals the trivial lower and upper bounds. We also give results on the mixed metric dimension of certain families of graphs and present an upper bound with respect to the girth of a graph. Finally, we prove that the problem of determining the mixed metric dimension of a graph is NP-hard in the general case.
Keywords: Hausdorff distance, distance between graphs, graph algorithms, trees, graph similarity, edge metric dimension, edge metric generator, mixed metric dimension, metric dimension
Published in DKUM: 03.08.2020; Views: 1467; Downloads: 115
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6.
Nature-inspired algorithms for hyperparameter optimization : magistrsko delo
Filip Glojnarić, 2019, master's thesis

Abstract: This master thesis is focusing on the utilization of nature-inspired algorithms for hyperparameter optimization, how they work and how to use them. We present some existing methods for hyperparameter optimization as well as propose a novel method that is based on six different nature-inspired algorithms: Firefly algorithm, Grey Wolf Optimizer, Particle Swarm Optimization, Genetic algorithm, Differential Evolution, and Hybrid Bat algorithm. We also show the optimization results (set of hyperparameters) for each algorithm and we present the plots of the accuracy for each combination and handpicked one. In discussion of the results, we provide the answers on our research questions as well as propose ideas for future work.
Keywords: artificial intelligence, artificial neural networks, machine learning, nature-inspired algorithms, evolutionary algorithms
Published in DKUM: 09.12.2019; Views: 1998; Downloads: 115
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7.
Differential evolution and large-scale optimization applications
Aleš Zamuda, scientific film, scientific sound or video publication

Abstract: Differential Evolution (DE) is one of the most popular, high-performance optimization algorithms with variants that have been outperforming others for years. As a result, DE has grown to accommodate wide usage for a variety of disciplines across scientific fields. Differential Evolution and Large-Scale Optimization Applications presents a research-based overview and cross-disciplinary applications of optimization algorithms. Emphasizing applications of Differential Evolution (DE) across sectors and laying the foundation for further use of DE algorithms in real-world settings, this video is an essential resource for researchers, engineers, and graduate-level students. Topics Covered : Algorithms, Optimization, Parallel Differential Evolution, Performance Improvement, Stochastic Methods, Tree Model Reconstruction.
Keywords: differential Evolution, optimization, algorithms, stochastic methods, tree models, tree model reconstruction
Published in DKUM: 14.05.2019; Views: 1659; Downloads: 217
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8.
Multi-objective optimization algorithms with the island metaheuristic for effective project management problem solving
Christina Brester, Ivan Ryzhikov, Eugene Semenkin, 2017, original scientific article

Abstract: Background and Purpose: In every organization, project management raises many different decision-making problems, a large proportion of which can be efficiently solved using specific decision-making support systems. Yet such kinds of problems are always a challenge since there is no time-efficient or computationally efficient algorithm to solve them as a result of their complexity. In this study, we consider the problem of optimal financial investment. In our solution, we take into account the following organizational resource and project characteristics: profits, costs and risks. Design/Methodology/Approach: The decision-making problem is reduced to a multi-criteria 0-1 knapsack problem. This implies that we need to find a non-dominated set of alternative solutions, which are a trade-off between maximizing incomes and minimizing risks. At the same time, alternatives must satisfy constraints. This leads to a constrained two-criterion optimization problem in the Boolean space. To cope with the peculiarities and high complexity of the problem, evolution-based algorithms with an island meta-heuristic are applied as an alternative to conventional techniques. Results: The problem in hand was reduced to a two-criterion unconstrained extreme problem and solved with different evolution-based multi-objective optimization heuristics. Next, we applied a proposed meta-heuristic combining the particular algorithms and causing their interaction in a cooperative and collaborative way. The obtained results showed that the island heuristic outperformed the original ones based on the values of a specific metric, thus showing the representativeness of Pareto front approximations. Having more representative approximations, decision-makers have more alternative project portfolios corresponding to different risk and profit estimations. Since these criteria are conflicting, when choosing an alternative with an estimated high profit, decision-makers follow a strategy with an estimated high risk and vice versa. Conclusion: In the present paper, the project portfolio decision-making problem was reduced to a 0-1 knapsack constrained multi-objective optimization problem. The algorithm investigation confirms that the use of the island meta-heuristic significantly improves the performance of genetic algorithms, thereby providing an efficient tool for Financial Responsibility Centres Management.
Keywords: 0-1 multi-objective constrained knapsack problem, project management portfolio problem, multi-objective evolution-based optimization algorithms, collaborative and cooperative meta-heuristics
Published in DKUM: 04.05.2018; Views: 1446; Downloads: 293
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9.
Limit cycle bifurcated from a center in a three dimensional system
Bo Sang, Brigita Ferčec, Qin-Long Wang, 2016, original scientific article

Abstract: Based on the pseudo-division algorithm, we introduce a method for computing focal values of a class of 3-dimensional autonomous systems. Using the $Є^1$-order focal values computation, we determine the number of limit cycles bifurcating from each component of the center variety (obtained by Mahdi et al). It is shown that at most four limit cycles can be bifurcated from the center with identical quadratic perturbations and that the bound is sharp.
Keywords: algorithms, three dimensional systems, focal value, limit cycle, Hopf bifurcation, center
Published in DKUM: 08.08.2017; Views: 2608; Downloads: 136
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10.
Computing the Szeged index
Janez Žerovnik, 1996, original scientific article

Abstract: We give an explicit algorithm for computing the Szeged index of a graph which runs in ▫$O(mn)$▫ time, where ▫$n$▫ is the number of nodes and ▫$m$▫ is the number of edges.
Keywords: mathematics, chemistry, chemical graph theory, molecular graphs, structural formulae, algorithms, topological index, Szeged index
Published in DKUM: 05.07.2017; Views: 1691; Downloads: 114
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