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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Topalov, Stefan Radović, Mirna University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia

ISBN: (纸本)9798331515799

This paper presents a modified Particle Swarm Optimization (PSO) algorithm designed to enhance computational efficiency without compromising solution quality. Two approaches are proposed: the first emphasizes the advantages of incorporating a variable population size to optimize both computational efficiency and solution quality. The second integrates this adaptive population strategy with the Nelder-Mead method to refine local search capabilities. By combining these techniques, the hybrid algorithm achieves a balance between global exploration and local exploitation, while also improving the algorithm's speed without sacrificing solution quality. The performance of both approaches is evaluated through numerical experiments, demonstrating their ability to deliver high-quality solutions with reduced computational resources. © 2025 IEEE.

关键词： Local search (optimization)

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Modification of the PSO Algorithm: Variable Population Size and Hybridization with the Nelder-Mead Method

Modification of the PSO Algorithm: Variable Population Size ...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Stefan Topalov Mirna Radović Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper presents a modified Particle Swarm Optimization (PSO) algorithm designed to enhance computational efficiency without compromising solution quality. Two approaches are proposed: the first emphasizes the advantages of incorporating a variable population size to optimize both computational efficiency and solution quality. The second integrates this adaptive population strategy with the Nelder-Mead method to refine local search capabilities. By combining these techniques, the hybrid algorithm achieves a balance between global exploration and local exploitation, while also improving the algorithm’s speed without sacrificing solution quality. The performance of both approaches is evaluated through numerical experiments, demonstrating their ability to deliver high-quality solutions with reduced computational resources.

关键词： Heuristic algorithms Stochastic processes Switches Linear programming Robustness Computational efficiency Optimization Standards Tuning Convergence

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Optimal control of Systems with Fractional Dynamics and Algebraic Constraints

Optimal Control of Systems with Fractional Dynamics and Alge...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Nikolina Živanović Zoran D. Jeličić Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This study investigates optimal control problems described by fractional differential equations, with the control vector components subject to algebraic constraints. Two case studies are analyzed: an illustrative example designed to test the algorithm’s effectiveness and a biologically significant model of acute myeloid leukemia (AML), which applies optimal control theory to design therapeutic strategies under medical constraints. The methodology transforms fractional differential equations into equivalent ordinary differential equations using the framework of Atanacković and Stanković. L.S. Pontryagin’s maximum principle is then applied to derive control functions that satisfy the algebraic constraints, followed by numerical optimization of the Hamiltonian function. We formally parsed the problem of dynamic optimization into a static optimization problem using the Forward-Backward Sweep Method (FBSM). The approach is validated through comprehensive numerical simulations, demonstrating its robustness and applicability to real-world problems.

关键词： Systematics Refining Optimal control Leukemia Transforms Mathematical models Robustness Vectors Reproducibility of results Optimization

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Evaluating Extended Kalman Filter and Unscented Kalman Filter for accurate trajectory estimation

Evaluating Extended Kalman Filter and Unscented Kalman Filte...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Anastasija Golić Mirna Radović Department of Computing and Control Engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

This paper evaluates two common methods for trajectory estimation: the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF). The EKF and UKF are well-established recursive filtering techniques commonly used for nonlinear state *** performance of these methods is compared through their implementation on a standard trajectory estimation problem, with a focus on assessing their accuracy.

关键词： Accuracy Linear approximation Trajectory Computational efficiency Kalman filters Reliability Nonlinear systems State estimation Tuning Standards

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Design of adaptive controllers by means of PPO algorithm using MATLAB 24

Design of adaptive controllers by means of PPO algorithm usi...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Radojičić, Veljko Rapaić, Milan R. University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia

ISBN: (纸本)9798331515799

This paper investigates reinforcement learning (RL) as a practical framework for achieving optimal adaptive control across several simple dynamical system models. All experiments were conducted using the Proximal Policy Optimization (PPO) algorithm, implemented within the MATLAB Reinforcement Learning Toolbox. The primary focus of this study is to explore how the learning process can be empirically designed to sufficiently excite the system dynamics and obtain a sufficiently robust controller. © 2025 IEEE.

关键词： Self adjusting control systems

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Optimal control of Systems with Fractional Dynamics and Algebraic Constraints 24

Optimal Control of Systems with Fractional Dynamics and Alge...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Živanović, Nikolina Jeličič, Zoran D. University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia

ISBN: (纸本)9798331515799

This study investigates optimal control problems described by fractional differential equations, with the control vector components subject to algebraic constraints. Two case studies are analyzed: an illustrative example designed to test the algorithm's effectiveness and a biologically significant model of acute myeloid leukemia (AML), which applies optimal control theory to design therapeutic strategies under medical constraints. The methodology transforms fractional differential equations into equivalent ordinary differential equations using the framework of Atanacković and Stanković. L.S. Pontryagin's maximum principle is then applied to derive control functions that satisfy the algebraic constraints, followed by numerical optimization of the Hamiltonian function. We formally parsed the problem of dynamic optimization into a static optimization problem using the Forward-Backward Sweep Method (FBSM). The approach is validated through comprehensive numerical simulations, demonstrating its robustness and applicability to real-world problems. © 2025 IEEE.

关键词： Ordinary differential equations

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Evaluating Extended Kalman Filter and Unscented Kalman Filter for accurate trajectory estimation 24

Evaluating Extended Kalman Filter and Unscented Kalman Filte...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Golić, Anastasija Radović, Mirna University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia

ISBN: (纸本)9798331515799

关键词： Extended Kalman filters

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Distill & Contrast: A New Graph Self-Supervised Method With Approximating Nature Data Relationships

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IEEE Transactions on Knowledge and Data engineering 2025年第6期37卷 3284-3297页

作者： He, Dongxiao Zhao, Jitao Guo, Rui Feng, Zhiyong Huo, Cuiying Jin, Di Pedrycz, Witold Zhang, Weixiong Tianjin University College of Intelligence and Computing Tianjin300350 China Department of Measurement and Control Systems Gliwice44-100 Poland University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Hong Kong Polytechnic University Department of Data Science and Ai Hong Kong

Contrastive Learning (CL) has emerged as a popular self-supervised representation learning paradigm that has been shown in many applications to perform similarly to traditional supervised learning methods. A key component of CL is mining the latent discriminative relationships between positive and negative samples and using them as self-supervised labels. We argue that this discriminative contrastive task is, in essence, similar to a classification task, and the "either positive or negative"hard label sampling strategies are arbitrary. To solve this problem, we explore ideas from data distillation, which considers probabilistic logit vectors as soft labels to transfer model knowledge. We attempt to abandon the classical hard sampling labels in CL and instead explore self-supervised soft labels. We adopt soft sampling labels that are extracted, without supervision, from the inherent relationships in data pairs to retain more information. We propose a new self-supervised graph learning method, Distill and Contrast (D&C), for learning representations that closely approximate natural data relationships. D&C extracts node similarities from the features and structures to derive soft sampling labels, which also eliminate noise in the data to increase robustness. Extensive experimental results on real-world datasets demonstrate the effectiveness of the proposed method. © 2025 IEEE.

关键词： Self-supervised learning

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Tiruveedula Gopi Krishna Nune Sreenivas M. Ajay Kumar Chandra Sekhar Reddy Davinder Sing Rathee Teklu Urgessa Department of Computer Science and Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of Electrical and Electronics Engineering Andhra Loyala institute of Engineering and Technology Vijayawada India Department of Electrical Power and Control Engineering College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of ECE College of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods.

关键词： Support vector machines Image segmentation Adaptation models Accuracy Magnetic resonance imaging Machine learning Feature extraction Brain modeling Data models Numerical models

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Reactive Navigation of the Ackermann Steering Robot in Unknown Environments

Reactive Navigation of the Ackermann Steering Robot in Unkno...

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International Conference on Autonomous Robots and Agents, ICARA

作者： ANA ŠELEK IVAN HRABAR SAŠA ŠTEKOVIĆ Department of Control and Computer Engineering Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Autegra D.O.O. Zagreb Croatia

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

Mobile robots are now widely used in numerous real-world applications that have complex navigation requirements, especially in environments used by humans. This requires highly accurate navigation that can be performed in realtime. In this paper, a method for generating a smooth motion of nonholonomic mobile robots is proposed. It enables robots to move optimally toward the desired goal and allows fast path replanning when encountering unknown or dynamic obstacles. The method generates smooth, collision-free trajectories based on clothoids, ensuring high computational efficiency and suitability for realtime path planning. By applying a smoothing algorithm, the proposed method improves the robot's efficiency in terms of travel time and trajectory length from start to goal, as demonstrated by a comparison with model predictive control.

关键词： Smoothing methods Navigation Dynamics Real-time systems Trajectory Computational efficiency Mobile robots Collision avoidance Testing Predictive control

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