1. Improving AGV path planning efficiency using Genetic Algorithms with Hamming distance-based initializationŽiga Breznikar, Janez Gotlih, Ž. Artič, Miran Brezočnik, 2025, izvirni znanstveni članek Opis: This paper presents a Genetic Algorithm (GA) framework for warehouse navigation as a Travelling Salesman Problem (TSP) variant for Automated Guided Vehicles (AGVs). The warehouse layout is represented as a graph, where pick-up locations serve as terminal nodes. A distance matrix, computed via Breadth-First Search (BFS) enables efficient route evaluation. To promote diversity in the initial population, a Hamming distance-based vectorized initialization strategy is employed, ensuring that the chromosomes are maximally distinct. The GA balances exploration and exploitation by dynamically adjusting the fitness function. Early generations emphasize diversity, while later ones focus on solution refinement, improving convergence and avoiding premature stagnation. Our key contribution demonstrates that the Hamming distance-based approach achieves comparable or better results with significantly fewer chromosomes. This reduces computational cost and runtime, making the method well-suited for real-time AGV routing in warehouses. The framework is adaptable to structured environments and shows strong potential for integration into real-world logistics and robotics applications. Future work will focus on optimizing the algorithm and integrating it into the ROS 2 environment.
Ključne besede: automated guided vehicles (AGV), warehouse routing, combinatorial optimization, Hamming distance initialization, Robot operating system 2 (ROS 2)
Objavljeno v DKUM: 28.11.2025; Ogledov: 0; Prenosov: 2
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2. Cogging torque reduction techniques in axial flux permanent magnet machines : a reviewFranjo Pranjić, Peter Virtič, 2024, pregledni znanstveni članek Opis: Axial flux permanent magnet machines have garnered significant attention in recent years due to their numerous advantages in various applications, including electric vehicles, wind turbines, and robotics. However, one of the critical challenges associated with these machines is the presence of cogging torque, which can hinder their efficiency and performance. This review article provides a comprehensive overview of the state-of-the-art techniques employed for cogging torque reduction in Axial Flux Permanent Magnet Machines. Different techniques are described, encompassing geometric optimization, magnet placement, and skewing methods. Firstly, the significance of Axial Flux Permanent Magnet Machines is described, as well as the issue of the cogging torque. In the methods section, a review of the strategies for the reduction of cogging torque is described from various articles, and finally, in the discussion section, a list of actions is presented for cogging torque reduction for different topologies. The novelty of the study is that it combines strategies for cogging torque reduction in a single article.
Ključne besede: cogging torque, axial flux permanent magnet machines, geometric optimization, magnet placement strategies, skewing techniques
Objavljeno v DKUM: 06.11.2025; Ogledov: 0; Prenosov: 3
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3. Temporal and statistical insights into multivariate time series forecasting of corn outlet moisture in industrial continuous-flow drying systemsMarko Simonič, Simon Klančnik, 2025, izvirni znanstveni članek Opis: Corn drying is a critical post-harvest process to ensure product quality and compliance with moisture standards. Traditional optimization approaches often overlook dynamic interactions between operational parameters and environmental factors in industrial continuous flow drying systems. This study integrates statistical analysis and deep learning to predict outlet moisture content, leveraging a dataset of 3826 observations from an operational dryer. The effects of inlet moisture, target air temperature, and material discharge interval on thermal behavior of the system were evaluated through linear regression and t-test, which provided interpretable insights into process dependencies. Three neural network architectures (LSTM, GRU, and TCN) were benchmarked for multivariate time-series forecasting of outlet corn moisture, with hyperparameters optimized using grid search to ensure fair performance comparison. Results demonstrated GRU’s superior performance in the context of absolute deviations, achieving the lowest mean absolute error (MAE = 0.304%) and competitive mean squared error (MSE = 0.304%), compared to LSTM (MAE = 0.368%, MSE = 0.291%) and TCN (MAE = 0.397%, MSE = 0.315%). While GRU excelled in average prediction accuracy, LSTM’s lower MSE highlighted its robustness against extreme deviations. The hybrid methodology bridges statistical insights for interpretability with deep learning’s dynamic predictive capabilities, offering a scalable framework for real-time process optimization. By combining traditional analytical methods (e.g., regression and t-test) with deep learning-driven forecasting, this work advances intelligent monitoring and control of industrial drying systems, enhancing process stability, ensuring compliance with moisture standards, and indirectly supporting energy efficiency by reducing over drying and enabling more consistent operation.
Ključne besede: advanced drying technologies, continuous flow drying, time-series forecasting, LSTM, GRU, TCN, deep learning, statistical analysis, optimization of the drying process
Objavljeno v DKUM: 03.11.2025; Ogledov: 0; Prenosov: 3
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4. OpenHENS: an open-source tool for heat exchanger network synthesisKeegan Keysers Hall, Andreja Nemet, Zdravko Kravanja, Timothy Gordon Walmsley, 2026, izvirni znanstveni članek Opis: The synthesis of heat exchanger networks (HENs) is an NP hard problem, made even more difficult by the requirement of commercial software licenses and coding ability. This paper introduces OpenHENS, a first-of-itskind open-source tool for HEN synthesis. In the literature, HEN synthesis based on mathematical programming almost exclusively relies on commercial MINLP (mixed-integer non-linear programming) solvers (e.g., BARON, Gurobi, etc.). Open source MINLP solvers, in contrast, lack the robustness, scalability and quality that are the hallmarks of commercial solvers. To overcome this challenge, OpenHENS embeds a novel three-step synthesis method that gradually increases the complexity of the model. The first two steps identify economically viable and thermodynamically feasible heat exchanger matches, removing the non-viable matches and reducing the problem size. In the third step, numerous HEN designs are obtained by evolving promising networks to obtain families of near-optimal solutions. OpenHENS was tested on thirteen benchmark problems and seven of the solutions were within 2 % of the total annualised cost (TAC) best-known solutions from literature. In eleven of the benchmark problems, OpenHENS returned more than 10 unique networks within 2 % of the best solution, enabling the engineer to select the most practical design with minimal cost difference. OpenHENS demonstrates that open-source software, when developed correctly, offers comparable performance to commercial software while promoting greater accessibility in industry.
Ključne besede: heat exchanger network, process integration, optimization, mathematical programming, open source, Phyton
Objavljeno v DKUM: 03.11.2025; Ogledov: 0; Prenosov: 1
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5. 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
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6. Connectivity with uncertainty regions given as line segmentsSergio Cabello, David Gajser, 2024, izvirni znanstveni članek Opis: For a set $\mathcal{Q}$ of points in the plane and a real number $δ$ ≥ 0, let $\mathbb{G}_δ(\mathcal{Q})$ be the graph defined on $\mathcal{Q}$ by connecting each pair of points at distance at most $δ$. We consider the connectivity of $\mathbb{G}_δ(\mathcal{Q})$ in the best scenario when the location of a few of the points is uncertain, but we know for each uncertain point a line segment that contains it. More precisely, we consider the following optimization problem: given a set $\mathcal{P}$ of $n$ – $k$ points in the plane and a set $\mathcal{S}$ of $k$ line segments in the plane, find the minimum $δ$ ≥ 0 with the property that we can select one point $p_s$ ∈ $s$ for each segment $s$ ∈ $\mathcal{S}$ and the corresponding graph $\mathbb{G}_δ(\mathcal{P} ∪ \{p_s$ | $s ∈ \mathcal{S}\})$ is connected. It is known that the problem is NP-hard. We provide an algorithm to exactly compute an optimal solution in $\mathcal{O}( f (k)n$ ${\rm log}$ $n)$ time, for a computable function $f$ (·). This implies that the problem is FPT when parameterized by $k$. The best previous algorithm uses $\mathcal{O}((k!)^kk^{k+1} · n^{2k})$ time and computes the solution up to fixed precision.
Ključne besede: computational geometry, uncertainty, geometric optimization, fixed parameter tractability, parametric search
Objavljeno v DKUM: 21.10.2025; Ogledov: 0; Prenosov: 1
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7. 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: 4
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8. Toward explainable time-series numerical association rule mining : a case study in smart-agricultureIztok Fister, Sancho Salcedo-Sanz, Enrique Alexandre-Cortizo, Damijan Novak, Iztok Fister, Vili Podgorelec, Mario Gorenjak, 2025, izvirni znanstveni članek Opis: This paper defines time-series numerical association rule mining in smart-agriculture applications from an explainable-AI perspective. Two novel explainable methods are presented, along with a newly developed algorithm for time-series numerical association rule mining. Unlike previous approaches, such as fixed interval time-series numerical association, the proposed methods offer enhanced interpretability and an improved data science pipeline by incorporating explainability directly into the software library. The newly developed xNiaARMTS methods are then evaluated through a series of experiments, using real datasets produced from sensors in a smart-agriculture domain. The results obtained using explainable methods within numerical association rule mining in smart-agriculture applications are very positive.
Ključne besede: association rule mining, explainable artificial intelligence, XAI, numerical association rule mining, optimization algorithms
Objavljeno v DKUM: 27.08.2025; Ogledov: 0; Prenosov: 5
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9. 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
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10. Computationally efficient multi-objective optimization of an interior permanent magnet synchronous machine using neural networksMitja Garmut, Simon Steentjes, Martin Petrun, 2025, izvirni znanstveni članek Opis: Improving the power density of an interior permanent magnet synchronous machine requires a complex and comprehensive approach that includes electromagnetic and thermal aspects. To achieve that, a multi-objective optimization of the machine’s geometry was performed according to selected key performance indicators by using numerical and analytical models. The primary objective of this research was to create a computationally efficient and accurate alternative to a direct finite element method-based optimization. By integrating artificial neural networks as meta-models, we aimed to demonstrate their performance in comparison to existing State-of-the-Art approaches. The artificial neural network approach achieved a nearly 20-fold reduction compared with the finite element method-based approach in computation time while maintaining accuracy, demonstrating its effectiveness as a computationally efficient alternative. The obtained artificial neural network can also be reused for different optimization scenarios and for iterative fine-tuning, further reducing the computation time. To highlight the advantages and limitations of the proposed approach, a multi-objective optimization scenario was performed, which increased the power-to-mass ratio by 16.5%.
Ključne besede: interior permanent magnet synchronous machine, artificial neural network, metamodel, multi-objective optimization, finite element method
Objavljeno v DKUM: 08.08.2025; Ogledov: 0; Prenosov: 19
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