1. 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: 4
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2. 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: 6
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3. 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: 2
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4. 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: 4
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5. 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: 4
Celotno besedilo (4,16 MB) |
6. Innovative approaches to wear reduction in horizontal powder screw conveyors : a design of experiments-guided numerical studyMarko Motaln, Tone Lerher, 2024, izvirni znanstveni članek Opis: Numerical simulations play a vital role in the modern engineering industry, especially when faced with interconnected challenges such as particle interactions and the structural integrity of conveyor systems. This article focuses on the handling of materials and emphasizes the importance of using parametric numerical analysis to improve efficiency, reduce wear, and enhance the structural integrity of horizontal screw conveyors. Through the utilization of the Design of Experiments, we systematically investigated critical parameters such as screw pitch, clearance, wear, rotational velocity, and additional structural factors. This examination was carried out within a well-defined parametric framework, utilizing a combination of software tools provided by the Ansys suite and Minitab. The findings demonstrate the effectiveness of the Design of Experiments analysis in achieving improved performance and provide valuable insights for engineers and researchers involved in the design of conveyor systems. Furthermore, this comprehensive approach clarifies how conveyor systems respond to changes in parameters and highlights the complex interaction between transported particles and the conveyor system. We present a detailed analysis that clarifies the complex relationships and dependencies among different parameters, providing engineers and researchers with valuable insights. By understanding the interactions of these factors, the methodology provides not only results but also a strategic framework for advancing conveyor system design and engineering practices.
Ključne besede: discrete element method, design optimization, horizontal screw conveyors, parametric study, conveying equipment, bulk handling, bulk solids, abrasive wear, screw conveyor, FEA, performance analysis
Objavljeno v DKUM: 09.04.2024; Ogledov: 275; Prenosov: 59
Celotno besedilo (10,51 MB)
Gradivo ima več datotek! Več... |
7. NiaNetAD: Autoencoder architecture search for tabular anomaly detection powered by HPCSašo Pavlič, Sašo Karakatič, Iztok Fister, 2023, objavljeni znanstveni prispevek na konferenci Ključne besede: predictive maintenance, autoencoder, anomaly detection, nature-inspired algorithms, optimization, high-performance computing, unsupervised learning
Objavljeno v DKUM: 26.01.2024; Ogledov: 447; Prenosov: 0 |
8. Optimization of a call centre performance using the stochastic queueing modelsAlenka Brezavšček, Alenka Baggia, 2014, izvirni znanstveni članek Opis: Background: A call centre usually represents the first contact of a customer with a given company. Therefore, the quality of its service is of key importance. An essential factor of the call centre optimization is the determination of the proper number of operators considering the selected performance measure. Results of previous research show that this can be done using the queueing theory approach. Objectives: The paper presents the practical application of the stochastic queueing models aimed at optimizing a Slovenian telecommunication provider’s call centre.
Methods/Approach: The arrival and the service patterns were analysed, and it was concluded that the call centre under consideration can be described using the M/M/r {infinity/infinity/FIFO} queueing model.
Results: An appropriate number of operators were determined for different peak periods of the working day, taking into consideration the following four performance measures: the expected waiting time, the expected number of waiting customers, the probability that a calling customer will have to wait, and the call centre service level.
Conclusions: The obtained results prove the usefulness and applicability of the queueing models as a tool for a call centre performance optimization. In practice, all the data needed for such a mathematical analysis are usually provided. This paper is aimed at illustrating how such data can be efficiently exploited.
Ključne besede: call centre, service quality, performance measure, optimization, stochastic queueing models
Objavljeno v DKUM: 31.03.2017; Ogledov: 1691; Prenosov: 405
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9. Multi-objective optimization of automated storage and retrieval systemsTone Lerher, Matjaž Šraml, Matej Borovinšek, Iztok Potrč, 2013, izvirni znanstveni članek Opis: The multi-objective optimization of automated warehouse is discussed and evaluated in present paper. Since most of researchers in material handling community had performed optimization of decision variables with single objective function only (usually named with minimum travel time, maximum throughput capacity, minimum cost, etc.), the multi-objective optimization (time-cost-quality) will be presented in present research. For the optimization of decision variables in objective functions, the method with genetic algorithms is used. The main objective of our contribution is to determine the performance of the system according to the multi-objective optimization technique.
Ključne besede: automated warehouses, performance, multi-objective function, optimization
Objavljeno v DKUM: 10.07.2015; Ogledov: 1133; Prenosov: 72
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10. Modelling operational, economic and environmental performance of an air transport networkMilan Janić, 2003, izvirni znanstveni članek Opis: The paper models the operational, economic and environmental performance of an air transport network consisting of airports and air routes connecting them. The operational capacity represents the operational performance. Thresholds on the networkćs environmental burdens reflect the environmental performance. The economic performance comprises the networkćs profits. Modelling the network performance includes using integer programming techniques to maximise total network profits for given operational capacity and environmental constraints under conditions where environmental externalities are internalised.
Ključne besede: air transport network, performance, capacity, environment, profits, externalities, optimization
Objavljeno v DKUM: 05.06.2012; Ogledov: 1396; Prenosov: 101
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