1. Improving mutation strategies in differential evolution with a new pbest selection mechanismJan Popič, Borko Bošković, Janez Brest, 2025, izvirni znanstveni članek Opis: Differential evolution, which belongs to a group of population-based algorithms, has received a lot of research attention since its introduction in 1995. A population-based algorithm is required to guide individuals to visit potentially better basins of attraction in the search space when searching for a globally optimal solution. Additionally, individuals need to interact with each other during an evolutionary process to explore the search space effectively. In this paper, we propose a novel pbest selection mechanism for DE/current-to-pbest mutation strategy and its variants designed to enhance the potential for exploration of different attraction basins. The proposed mechanism enforces a minimal distance between the selected pbest individual and all other better individuals. This means that possible candidates for the pbest individual, used in mutation, are further spaced apart. As a result, the likelihood that the new trial vector will be generated in a different attraction basin of the search space is increased. The mechanism is incorporated into the L-SHADE, jSO, and L-SRTDE algorithms, and its effectiveness is evaluated using CEC’24 benchmark functions. Experimental results demonstrate improvements in the performance of the selected algorithms, particularly in higher-dimensional problem instances.
Ključne besede: population-based algorithm, differential evolution, gobal optimization, mutation strategies, exploration–exploitation
Objavljeno v DKUM: 29.10.2025; Ogledov: 0; Prenosov: 5
 Celotno besedilo (3,09 MB) |
2. Mobile robot localization based on the PSO algorithm with local minima avoiding the fitness functionBožidar Bratina, Dušan Fister, Suzana Uran, Izidor Mlakar, Erik Rot Weiss, Kristijan Korez, Riko Šafarič, 2025, izvirni znanstveni članek Opis: Localization of a semi-humanoid mobile robot Pepper is proposed based on the particle swarm optimization algorithm (PSO) that is robust to the disturbance perturbations of LIDAR-measured distances from the mobile robot to the walls of the robot real laboratory workspace. The novel PSO, with the avoiding local minima algorithm (PSO-ALM), uses a novel fitness function that can prevent the PSO search from trapping into the local minima and thus prevent the mobile robot from misidentifying the actual location. The fitness function penalizes nonsense solutions by introducing continuous integrity checks of solutions between two different consecutive locations. The proposed methodology enables accurate and real-time global localization of a mobile robot, given the underlying a priori map, with a consistent and predictable time complexity. Numerical simulations and real-world laboratory experiments with different a priori map accuracies have been conducted to prove the proper functioning of the method. The results have been compared with the benchmarks, i.e., the plain vanilla PSO and the built-in robot’s odometrical method, a genetic algorithm with included elitism and adaptive mutation rate (GA), the same GA algorithm with the included ALM algorithm (GA-ALM), the state-of-the-art plain vanilla golden eagle optimization (GEO) algorithm, and the same GEO algorithm with the added ALM algorithm (GEO-ALM). The results showed similar performance with the odometrical method right after recalibration and significantly better performance after some traveled distance. The GA and GEO algorithms with or without the ALM extension gave us similar results according to the accuracy of localization. The optimization algorithms’ performance with added ALM algorithms was much better at not getting caught in the local minimum, while the PSO-ALM algorithm gave us the overall best results
Ključne besede: mobile robot localization, PSO algorithm, avoid the global minima
Objavljeno v DKUM: 17.10.2025; Ogledov: 0; Prenosov: 5
Celotno besedilo (2,95 MB) |
3. Threshold adaptation for improved wrapper-based evolutionary feature selectionUroš Mlakar, Iztok Fister, Iztok Fister, 2025, izvirni znanstveni članek Opis: Feature selection is essential for enhancing classification accuracy, reducing overfitting, and improving interpretability in high-dimensional datasets. Evolutionary Feature Selection (EFS) methods employ a threshold parameter � to decide feature inclusion, yet the widely used static setting �=0.5 may not yield optimal results. This paper presents the first large-scale, systematic evaluation of threshold adaptation mechanisms in wrapper-based EFS across a diverse number of benchmark datasets. We examine deterministic, adaptive, and self-adaptive threshold parameter control under a unified framework, which can be used in an arbitrary bio-inspired algorithm. Extensive experiments and statistical analyses of classification accuracy, feature subset size, and convergence properties demonstrate that adaptive mechanisms outperform the static threshold parameter control significantly. In particular, they not only provide superior tradeoffs between accuracy and subset size but also surpass the state-of-the-art feature selection methods on multiple benchmarks. Our findings highlight the critical role of threshold adaptation in EFS and establish practical guidelines for its effective application.
Ključne besede: feature selection, evolutionary algorithm, feature threshold, evolutionary feature selection
Objavljeno v DKUM: 14.10.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (12,73 MB) |
4. Using data mining to improve decision-making : case study of a recommendation system developmentHyrmet Mydyti, Arbana Kadriu, Mirjana Pejić Bach, 2023, izvirni znanstveni članek Opis: Background and purpose: This study aims to provide a practical perspective on how data mining techniques are used in the home appliance after-sales services. Study investigates on how can a recommendation system help a customer service company that plans to use data mining to improve decision making during its digital transformation process. In addition, study provides a detailed outline on the process for developing and analyzing platforms to improve data analytics for such companies. Methodology: Case study approach is used for evaluating the usability of recommendation systems based on data mining approach in the context of home appliance after-sales services. We selected the latest platforms based on their relevance to the recommender system and their applicability to the functionality of the data mining system as trends in the system design. Results: Evaluation of the impact on decision making shows how the application of data mining techniques in organizations can increase efficiency. Evaluation of the time taken to resolve the complaint, as a key attribute of service quality that affects customer satisfaction, and the positive results achieved by the recommendation system are presented. Conclusion: This paper increases the understanding of the benefits of the data mining approach in the context of recommender systems. The benefits of data mining, an important component of advanced analytics, lead to an increase in business productivity through predictive analytics. For future research, other attributes or factors useful for the recommender systems can be considered to improve the quality of the results. Acknowledgement: The author Hyrmet Mydyti’s PhD thesis has been extended in this paper.
Ključne besede: digital transformation, data mining, decision tree algorithm, decision-making, home appliances after-sales services
Objavljeno v DKUM: 08.10.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (1,95 MB)
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5. Performance comparison of single-objective evolutionary algorithms implemented in different frameworksMiha Ravber, Marko Šmid, Matej Moravec, Marjan Mernik, Matej Črepinšek, 2025, izvirni znanstveni članek Opis: Fair comparison with state-of-the-art evolutionary algorithms is crucial, but is obstructed by differences in problems, parameters, and stopping criteria across studies. Metaheuristic frameworks can help, but often lack clarity on algorithm versions, improvements, or deviations. Some also restrict parameter configuration. We analysed source codes and identified inconsistencies between implementations. Performance comparisons across frameworks, even with identical settings, revealed significant differences, sometimes even with the authors’ own code. This questions the validity of comparisons using such frameworks. We provide guidelines to improve open-source metaheuristics, aiming to support more credible and reliable comparative studies.
Ključne besede: metaheuristics, evolutionary algorithm, metaheuristic optimization framework, algorithm comparison, benchmarking
Objavljeno v DKUM: 02.10.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (3,89 MB) |
6. PrProgramming industrial robots for milling aplications using off-line programming softwares (OLP)Domen Šošter, 2025, diplomsko delo Opis: This thesis explores the feasibility of using industrial robots for milling applications, focusing on the challenges of maintaining accuracy. The Yaskawa GP8 robot was used for milling soft materials, and the study emphasizes the importance of offline programming (OLP) for improving precision. The research shows that while robotic milling is viable for soft materials, milling rigid materials would require significant adjustments, including enhanced compensation techniques and calibration. Despite the limited time during the internship, the compensation algorithm was explored and successfully implemented in milling operations. This allowed for measurable improvements in the milling results, demonstrating the potential of the compensation algorithm in enhancing precision.
Ključne besede: Robotic milling, CAD, CAM, Off-line programming tools, path-correction algorithm
Objavljeno v DKUM: 22.09.2025; Ogledov: 0; Prenosov: 8
Celotno besedilo (4,21 MB) |
7. Efficient direct reconstruction of bipartite (multi)graphs from their line graphs through a characterization of their edgesDrago Bokal, Janja Jerebic, 2025, izvirni znanstveni članek Opis: We study the line graphs of bipartite multigraphs, which naturally arise in combinatorics,
game theory, and applications such as scheduling and motion planning. We introduce
a new characterization of these graphs via valid partial assignments of the edges of the
underlying bipartite multigraph to the vertices of its line graph. We show that an empty
assignment extends to a complete one precisely when the graph is a line graph of a bipartite
multigraph. Based on this, we design an O(∆(G)|E(G)|) algorithm that incrementally
constructs such assignments. The algorithm also provides a data structure supporting
efficient solutions to problems of maximum clique, maximum weighted clique, minimum
clique cover, chromatic number, and independence number. For line graphs of bipartite
simple graphs these problems become solvable in linear time, improving on previously
known polynomial-time results. For general bipartite multigraphs, our method enhances
the O(|V(G)|
3
) recognition algorithm of Peterson and builds on the results of Demaine et al.,
Hedetniemi, Cook et al., and Gurvich and Temkin.
Ključne besede: UNO-graph, line graph, bipartite graph, bipartite multigraph, graph algorithm
Objavljeno v DKUM: 09.09.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (348,95 KB)
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8. Controllability-oriented method to improve small-signal response of virtual synchronous generatorsAntonija Šumiga, Boštjan Polajžer, Jožef Ritonja, Peter Kitak, 2025, izvirni znanstveni članek Opis: This paper presents a method for optimizing the inertia constants and damping coefficients of interconnected virtual synchronous generators (VSGs) using a genetic algorithm. The goal of optimization is to find a balance between minimizing the rate of change of frequency (RoCoF) and enhancing controllability. Five controllability-based metrics are tested: the minimum eigenvalue, the sum of the two smallest eigenvalues, the maximum eigenvalue, the trace, and the determinant of the controllability Gramian matrix. The approach includes the oscillatory modes’ damping ratio constraints to ensure the small-signal stability of the entire system. The results of optimization on the IEEE 9-bus system with three VSGs show that the proposed method improves controllability, reduces RoCoF, and maintains the desired oscillation damping. The proposed approach was tested through time-domain simulations.
Ključne besede: virtual synchronous generator, inertia constant, damping coefficient, small-signal stability, multi-objective optimization, genetic algorithm
Objavljeno v DKUM: 12.08.2025; Ogledov: 0; Prenosov: 11
Celotno besedilo (5,26 MB) |
9. Comparative analysis of nonlinear models developed using machine learning algorithmsMaja Rožman, Alen Kišić, Dijana Oreški, 2024, izvirni znanstveni članek Opis: Machine learning algorithms are increasingly used in a vast spectrum of domains where statistical approaches were previously used. Algorithms such as artificial neural networks, classification, regression trees, or support vector machines provide various advantages over traditional linear regression or discriminant analysis. Advantages such as flexibility, scalability, and improved accuracy in dealing with diverse data types, nonlinear problems, and dimensionality reduction, compared to traditional statistical methods are empirically demonstrated in many previous research papers. In this paper, two machine learning algorithms are compared with one statistical method on highly nonlinear data. Results indicate a high level of effectiveness for machine learning algorithms when dealing with nonlinearity.
Ključne besede: machine learning, decision tree algorithm, artificial neural network, predictive models, data characteristics, nonlinear data, artificial intelligence
Objavljeno v DKUM: 02.07.2025; Ogledov: 0; Prenosov: 7
Celotno besedilo (730,52 KB)
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10. Optimizing berth allocation for maritime autonomous surface ships (MASSs) in the context of mixed operation scenariosShen Lixin, Xueting Shu, Chengcheng Li, Tomaž Kramberger, Xiaoguang Li, Lixing Jiang, 2025, izvirni znanstveni članek Opis: This study deals with berth allocation for Maritime Autonomous Surface Ships (MASSs) in the context of the mixed operation of MASSs and manned vessels from the perspective of port-shipping companies’ collaboration. Two berth allocation strategies, namely the separated-type and the mixed-type, are proposed in this article. Two mixed integer nonlinear programming models aimed at minimizing the total docking cost of the vessels in the port and the waiting time for berths are developed and solved using Gurobi, respectively. A large-scale simulation of the mixed-type berth allocation model is carried out using an improved simulated annealing algorithm. Several experiments are conducted to test the effectiveness of the model and to draw insights for commercializing autonomous vessels. The presented results show that multi-objective modeling and optimization should be conducted from the collaboration of port-shipping companies, which is more efficient from the perspective of shipping companies or ports, respectively. When berth resources are limited or there is a high requirement for operational safety, the separated-type berth allocation strategy is more efficient. When the number of MASS-dedicated berths reaches a certain proportion, the total docking cost of the vessel no longer changes, indicating that more dedicated berths are not better.
Ključne besede: maritime autonomous surface ship (MASS), mixed operation scenarios, berth allocation, mixed integer programming, simulated annealing algorithm, port-shipping companies’ collaboration
Objavljeno v DKUM: 18.06.2025; Ogledov: 0; Prenosov: 1
Celotno besedilo (5,17 MB)
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