1. Maintenance management of a transmission substation with optimizationPeter Kitak, Lovro Belak, Jože Pihler, Janez Ribič, 2021, izvirni znanstveni članek Opis: The paper deals with the reliability-centered maintenance (RCM) of a transmission substation. The process of the planning and actual performance of maintenance was carried out using an
optimization algorithm. This maintenance procedure represents the maintenance management and
included reliability of the power system operation, maintenance costs, and associated risks. The originality of the paper lies in the integrated treatment of all maintenance processes that are included in
the pre-processing and used in the optimization process for reliability-centered maintenance. The optimization algorithm of transmission substation maintenance was tested in practice on the equipment
and components of an existing 400/110–220/110 kV substation in the Slovenian electricity transmission system. A comparison analysis was also carried out of the past time-based maintenance (TBM)
and the new reliability-centered maintenance (RCM), on the basis of the optimization algorithm.
Ključne besede: reliability-centered maintenance, optimization, transmission substation, condition monitoring
Objavljeno v DKUM: 20.06.2025; Ogledov: 0; Prenosov: 1
 Celotno besedilo (1,74 MB)
Gradivo ima več datotek! Več... |
2. Constrained multi-objective optimization of simulated tree pruning with heterogeneous criteriaDamjan Strnad, Štefan Kohek, 2021, izvirni znanstveni članek Opis: Virtual pruning of simulated fruit tree models is a useful functionality provided by software tools for computer-aided horticultural education and research. It also enables algorithmic
pruning optimization with respect to a set of quantitative objectives, which is important for analytical
purposes and potential applications in automated pruning. However, the existing studies in pruning
optimization focus on a single type of objective, such as light distribution within the crown. In this
paper, we propose the use of heterogeneous objectives for discrete multi-objective optimization of
simulated tree pruning. In particular, the average light intake, crown shape, and tree balance are
used to observe the emergence of different pruning patterns in the non-dominated solution sets. We
also propose the use of independent constraint objectives as a new mechanism to confine overfitting
of solutions to individual pruning criteria. Finally, we perform the comparison of NSGA-II, SPEA2,
and MOEA/D-EAM on this task. The results demonstrate that SPEA2 and MOEA/D-EAM, which
use external solution archives, can produce better sets of non-dominated solutions than NSGA-II.
Ključne besede: multi-objective optimization, virtual tree pruning, heterogeneous objectives, constraint objectives, NSGA-II, SPEA2, EuMOEA/D-EAM
Objavljeno v DKUM: 19.06.2025; Ogledov: 0; Prenosov: 0
Celotno besedilo (5,75 MB)
Gradivo ima več datotek! Več... |
3. Overcoming stagnation in metaheuristic algorithms with MsMA’s adaptive meta-level partitioningMatej Črepinšek, Marjan Mernik, Miloš Beković, Matej Pintarič, Matej Moravec, Miha Ravber, 2025, izvirni znanstveni članek Opis: Stagnation remains a persistent challenge in optimization with metaheuristic algorithms (MAs), often leading to premature convergence and inefficient use of the remaining evaluation budget. This study introduces , a novel meta-level strategy that externally monitors MAs to detect stagnation and adaptively partitions computational resources. When stagnation occurs, divides the optimization run into partitions, restarting the MA for each partition with function evaluations guided by solution history, enhancing efficiency without modifying the MA’s internal logic, unlike algorithm-specific stagnation controls. The experimental results on the CEC’24 benchmark suite, which includes 29 diverse test functions, and on a real-world Load Flow Analysis (LFA) optimization problem demonstrate that MsMA consistently enhances the performance of all tested algorithms. In particular, Self-Adapting Differential Evolution (jDE), Manta Ray Foraging Optimization (MRFO), and the Coral Reefs Optimization Algorithm (CRO) showed significant improvements when paired with MsMA. Although MRFO originally performed poorly on the CEC’24 suite, it achieved the best performance on the LFA problem when used with MsMA. Additionally, the combination of MsMA with Long-Term Memory Assistance (LTMA), a lookup-based approach that eliminates redundant evaluations, resulted in further performance gains and highlighted the potential of layered meta-strategies. This meta-level strategy pairing provides a versatile foundation for the development of stagnation-aware optimization techniques.
Ključne besede: optimization, metaheuristics, stagnation, meta-level strategy, algorithmic performance, duplicate solutions
Objavljeno v DKUM: 30.05.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (1,47 MB) |
4. High-performance deployment operational Data analytics of pre-trained multi-label classification architectures with differential-evolution-based hyperparameter optimization (AutoDEHypO)Teo Prica, Aleš Zamuda, 2025, izvirni znanstveni članek Opis: This article presents a high-performance-computing differential-evolution-based hyperparameter optimization automated workflow (AutoDEHypO), which is deployed on a petascale supercomputer and utilizes multiple GPUs to execute a specialized fitness function for machine learning (ML). The workflow is designed for operational analytics of energy efficiency. In this differential evolution (DE) optimization use case, we analyze how energy efficiently the DE algorithm performs with different DE strategies and ML models. The workflow analysis considers key factors such as DE strategies and automated use case configurations, such as an ML model architecture and dataset, while monitoring both the achieved accuracy and the utilization of computing resources, such as the elapsed time and consumed energy. While the efficiency of a chosen DE strategy is assessed based on a multi-label supervised ML accuracy, operational data about the consumption of resources of individual completed jobs obtained from a Slurm database are reported. To demonstrate the impact on energy efficiency, using our analysis workflow, we visualize the obtained operational data and aggregate them with statistical tests that compare and group the energy efficiency of the DE strategies applied in the ML models.
Ključne besede: high-performance computing, operational data analytics, energy efficiency, machine learning, AutoML, differential avolution, optimization
Objavljeno v DKUM: 29.05.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (1,61 MB) |
5. Incorporating enriched empirical models into optimization algorithm to enhance biogas productionTina Kegl, Andreja Goršek, Darja Pečar, 2025, izvirni znanstveni članek Opis: This paper introduces a novel approach to optimization of the anaerobic co-digestion (AcoD) process by developing enriched versions of first-order kinetic, modified Gompertz, and single-stage combined kinetic models. The key innovation of these enriched models lies in the introduction of new kinetic parameters that depend on both temperature and substrate composition, resulting in a set of new model parameters. These parameters are calibrated simultaneously across various process conditions, unlike existing models where kinetic constants are calibrated for only one operating regime. The enriched models are successfully calibrated and validated with experimental data from a batch AcoD of chicken manure with sawdust and fungal-pretreated Miscanthus; the relative index of agreement is higher than 0.99 for the produced biogas under all considered process conditions. By using the calibrated models to optimize the substrate composition and the AcoD process temperature profile, the results indicate that biogas production can increase by up to 50 %. Moreover, the proposed optimization allows for a favorable cost-benefit ratio; the estimated net energy gain can increase by up to 40 %. The proposed enriched models enable accurate prediction of biogas production at various process conditions and optimization of the AcoD process, representing a significant advancement over existing empirical models.
Ključne besede: biogas production, Pleurothus ostreatus, kinetic model parameters calibration, process optimization, gradient-based optimization, energy trade-offs
Objavljeno v DKUM: 29.05.2025; Ogledov: 0; Prenosov: 0
 Povezava na datoteko |
6. State-of-the-art cross-platform mobile application development frameworks : a comparative study of market and developer trendsGregor Jošt, Viktor Taneski, 2025, pregledni znanstveni članek Opis: Cross-platform mobile application development has gained significant traction in recent years, driven by the growing demand for efficient, cost-effective solutions that cater to both iOS and Android platforms. This paper presents a state-of-the-art review of crossplatform mobile application development, emphasizing the industry trends, framework popularity, and adoption in the job market. By analyzing developer preferences, community engagement, and market demand, this study provides a comprehensive overview of how cross-platform mobile development frameworks shape the mobile development landscape. The research employs a data-driven methodology, drawing insights from three key categories: Developer Sentiment and Survey Data, Community Engagement and Usage Data, and Market Adoption and Job Market Data. By analyzing these factors, the study identifies the key challenges and emerging trends shaping cross-platform mobile application development. It assesses the most widely used frameworks, comparing their strengths and weaknesses in real-world applications. Furthermore, the research examines the industry adoption patterns and the presence of these frameworks in job market trends. Unlike earlier research, which included now-obsolete platforms like Windows Phone and frameworks such as Xamarin, this study is tailored to the current cross-platform mobile application development market landscape. The conclusions offer actionable insights for developers and researchers, equipping them with the knowledge needed to navigate the evolving cross-platform mobile application development ecosystem effectively.
Ključne besede: cross-platform mobile application development, flutter, react native, .NET MAUI, mobile app engineering, framework evolution, performance optimization, developer experience, job market trends
Objavljeno v DKUM: 29.05.2025; Ogledov: 0; Prenosov: 0
Celotno besedilo (1,80 MB) |
7. Application of digital tools assessing information risk in the control activitySilviya Kostova, Zhelyo Zhelev, 2024, izvirni znanstveni članek Opis: The paper discusses digitising the leading information flows in control activities. The focus is on applying technology and its integration in implementing forms of control. The aim is to argue for practical approaches to minimise information risk in pre-ongoing and postcontrol to ensure the accuracy and veracity of financial and nonfinancial information. Evaluating the effectiveness of data ensures integrity, consistency, validity, completeness and timeliness - applying digital risk assessment tools to control activities. To prove the hypotheses, the cross-tabulation method is applied, focusing on the relationship between the use of verification, inspection, audit, revision and supervision, and the digital tools applied in the control institutions implementing financial control in the public sector of the Republic of Bulgaria. The study evaluates the effectiveness of data management practices, emphasizing the importance of maintaining integrity, consistency, validity, completeness, and timeliness. The study acknowledges that the use of advanced digital risk assessment tools could improve the effectiveness of control activities in various areas. The approach supports the sustainability of financial control and is in line with modern management standards, promoting a culture of accountability and precision in the management of public finances.
Ključne besede: digital risk assessment, information integrity, public financial management, optimization accountability
Objavljeno v DKUM: 28.05.2025; Ogledov: 0; Prenosov: 0
Celotno besedilo (344,46 KB)
Gradivo ima več datotek! Več... |
8. Comparative study of modern differential evolution algorithms : perspectives on mechanisms and performanceJanez Brest, Mirjam Sepesy Maučec, 2025, izvirni znanstveni članek Opis: Since the discovery of the Differential Evolution algorithm, new and improved versions have continuously emerged. In this paper, we review selected algorithms based on Differential Evolution that have been proposed in recent years. We examine the mechanisms integrated into them and compare the performance of algorithms. To compare their performances, statistical comparisons were used as they enable us to draw reliable conclusions about the algorithms’ performances. We use the Wilcoxon signed-rank test for pairwise comparisons and the Friedman test for multiple comparisons. Subsequently, the Mann–Whitney U-score test was added. We conducted not only a cumulative analysis of algorithms, but we also focused on their performances regarding the function family (i.e., unimodal, multimodal, hybrid, and composition functions). Experimental results of algorithms were obtained on problems defined for the CEC’24 Special Session and Competition on Single Objective Real Parameter Numerical Optimization. Problem dimensions of 10, 30, 50, and 100 were analyzed. In this paper, we highlight promising mechanisms for further development and improvements based on the study of the selected algorithms.
Ključne besede: global optimization, differential evolution, benchmark suite, mechanisms, statistical tests, performance
Objavljeno v DKUM: 19.05.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (313,93 KB) |
9. Tackling blind spot challenges in metaheuristics algorithms through exploration and exploitationMatej Črepinšek, Miha Ravber, Luka Mernik, Marjan Mernik, 2025, izvirni znanstveni članek Opis: This paper defines blind spots in continuous optimization problems as global optima that are inherently difficult to locate due to deceptive, misleading, or barren regions in the fitness landscape. Such regions can mislead the search process, trap metaheuristic algorithms (MAs) in local optima, or hide global optima in isolated regions, making effective exploration particularly challenging. To address the issue of premature convergence caused by blind spots, we propose LTMA+ (Long-Term Memory Assistance Plus), a novel meta-approach that enhances the search capabilities of MAs. LTMA+ extends the original Long-Term Memory Assistance (LTMA) by introducing strategies for handling duplicate evaluations, shifting the search away from over-exploited regions and dynamically toward unexplored areas and thereby improving global search efficiency and robustness. We introduce the Blind Spot benchmark, a specialized test suite designed to expose weaknesses in exploration by embedding global optima within deceptive fitness landscapes. To validate LTMA+, we benchmark it against a diverse set of MAs selected from the EARS framework, chosen for their different exploration mechanisms and relevance to continuous optimization problems. The tested MAs include ABC, LSHADE, jDElscop, and the more recent GAOA and MRFO. The experimental results show that LTMA+ improves the success rates for all the tested MAs on the Blind Spot benchmark statistically significantly, enhances solution accuracy, and accelerates convergence to the global optima compared to standard MAs with and without LTMA. Furthermore, evaluations on standard benchmarks without blind spots, such as CEC’15 and the soil model problem, confirm that LTMA+ maintains strong optimization performance without introducing significant computational overhead.
Ključne besede: optimization, metaheuristics algorithm, algorithmic performance, duplicate solutions, nonrevisited solutions, blind spots, LTMA
Objavljeno v DKUM: 19.05.2025; Ogledov: 0; Prenosov: 2
Celotno besedilo (4,16 MB) |
10. Integrating Multi-Physics Modeling within Multi-Objective Optimization to Enhance the Performance and Efficiency of Permanent Magnet Synchronous Machines : doktorska disertacijaMitja Garmut, 2025, doktorska disertacija Opis: This Dissertation focuses on the optimization of an Interior Permanent Magnet (IPM) machine for handheld battery-powered tools, aiming to enhance performance and efficiency.
The research integrates multi-physics modeling, including electromagnetic Finite Element Method (FEM) and thermal models, to evaluate machine performance under various operating conditions. The performance is evaluated according to selected Key Performance Indicators (KPIs). Further, different control methods, such as Field Oriented Control and Square-Wave Control, impact the performance significantly and are incorporated into the optimization process.
Due to the computational challenges of FEM-based performance evaluations in Multi-Objective Optimization (MOO), this work utilizes Artificial Neural Network (ANN)-based meta-models, to accelerate the optimization process while preserving accuracy.
The developed meta-models capture nonlinear machine characteristics from the FEM model. These meta-models are then used to evaluate machine performance through a combination of analytical and numerical post-processing methods.
Four MOO scenarios are presented, each aimed at optimizing the cross-sectional design of IPM machines, to enhance performance and efficiency while reducing mass and cost. Additionally, these scenarios modify the machine’s electromagnetic behavior, to ensure better alignment with the selected control method.
By comparing the optimization process of Scenario 1, which uses direct FEM-based evaluation without time reduction measures, to the approach incorporating Artificial Neural Network based meta-models, the total number of individual FEM evaluations decreased from 2.35×10^9 to 2.03×10^5, without almost any loss of accuracy. This reduced the computation time from 297 years to 9.07 days on our standard desktop computer. The obtained ANN-base meta-models can be used further for other optimizations without the need for additional FEM evaluations.
In all four optimization scenarios, the use of meta-models enabled the generation of a Pareto front of the optimal solutions, leading to improved KPIs compared to the reference design. The highest relative improvement occurred in Scenario 1, where the selected optimized machine design achieved a 30% increase in power density compared to the reference design.
Ključne besede: Interior Permanent Magnet (IPM) Machine, Artificial Neural Network (ANN), Meta-Modeling, Multi-Objective Optimization (MOO), Finite Element Method (FEM), Multi-Physics Modeling, Field Oriented Control (FOC), Square-Wave Control (SWC)
Objavljeno v DKUM: 15.05.2025; Ogledov: 0; Prenosov: 39
Celotno besedilo (17,79 MB) |
