Piotr Dziwinski

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Faculty of Computer Science and Artificial Intelligence
Czestochowa University of Technology

Piotr Dziwinski


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https://researchid.co/pdziwinski4

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Science, Computer Graphics and Computer-Aided Design, Computer Science Applications
26

Scopus Publications

Scopus Publications

  • A new Hybrid Dynamic Tournament Topology Particle Swarm Optimizaton with Parameter Independence
    Piotr Dziwiński
    Gecco 2025 Companion Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion, 2025
    Particle Swarm Optimization (PSO) is a population-based optimization algorithm inspired by the social behavior of birds, with widespread applications in diverse domains. While PSO demonstrates rapid convergence and resilience against local minima, its performance is susceptible to parameter values and prone to stagnation in complex search spaces. This paper introduces a Hybrid Dynamic Tournament Topology Particle Swarm Optimization with Parameter Independence (HDTT-PSO-PI), integrating a novel velocity update equation and dynamic tournament topology for neighbor selection. The velocity formulation replaces original parameters ω, and ϕ of the DTT-PSO algorithm with α, β, and cs, enabling independent control of particle dynamics and better adaptability across problem landscapes. The HDTT-PSO-PI algorithm further enhances performance by employing genetic operators for increased solution diversity. A comprehensive evaluation of benchmark functions demonstrates the superior performance of HDTT-PSO-PI compared to 15 state-of-the-art evolutionary and swarm algorithms. Parameter optimization experiments highlight the algorithm's robustness across a wide range of settings, significantly improving performance and solution accuracy. These findings confirm the HDTT-PSO-PI algorithm as a promising approach for solving complex, unimodal, and multimodal optimization problems. The results suggest future research opportunities in hybridization and parameter adaptation strategies for swarm-based algorithms.
  • A new Hybrid Parameter Independent Particle Swarm Optimization Algorithm
    Piotr Dziwiński
    Gecco 2024 Companion Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion, 2024
    The Particle Swarm Optimization (PSO) algorithm is a well-known and widely used technique for solving complex optimization problems, often providing very good results. However, precise parameter selection or auto-adaptation mechanisms are crucial for ensuring the optimal performance of the algorithm. Unfortunately, adjusting the parameter values is difficult due to their mutual dependence.
  • A New Hybrid Particle Swarm Optimization and Evolutionary Algorithm with Self-Adaptation Mechanism
    Piotr Dziwiński, Łukasz Bartczuk
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2023
  • Efficient Lightweight Multimodel Deep Fusion Based on ECG for Arrhythmia Classification
    Mohamed Hammad, Souham Meshoul, Piotr Dziwiński, Paweł Pławiak, Ibrahim A. Elgendy
    Sensors, 2022
    An arrhythmia happens when the electrical signals that organize the heartbeat do not work accurately. Most cases of arrhythmias may increase the risk of stroke or cardiac arrest. As a result, early detection of arrhythmia reduces fatality rates. This research aims to provide a lightweight multimodel based on convolutional neural networks (CNNs) that can transfer knowledge from many lightweight deep learning models and decant it into one model to aid in the diagnosis of arrhythmia by using electrocardiogram (ECG) signals. Thus, we gained a multimodel able to classify arrhythmia from ECG signals. Our system’s effectiveness is examined by using a publicly accessible database and a comparison to the current methodologies for arrhythmia classification. The results we achieved by using our multimodel are better than those obtained by using a single model and better than most of the previous detection methods. It is worth mentioning that this model produced accurate classification results on small collection of data. Experts in this field can use this model as a guide to help them make decisions and save time.
  • Fast Estimation of Multidimensional Regression Functions
    Tomasz Galkowski, Adam Krzyzak, Piotr Dziwinski
    2022 17th International Conference on Control Automation Robotics and Vision Icarcv 2022, 2022
    Various methods for fitting an unknown functions from the set of noisy measurements are applicable to a wide variety of problems. Among them, the nonparametric algorithms based on the Parzen kernel are willingly used. In the article, we propose a novel and very effective numerical simplification in Parzen approach leading to a significant reduction in computation time. The algorithm is basically developed for multidimensional case. The two-dimensional version of the method is explained in details and analysed. Computational complexity and speed of convergence of the algorithm are studied. Some applications for solving real problems with our algorithms are presented.
  • Hardware implementation of a Takagi-Sugeno neuro-fuzzy system optimized by a population algorithm
    Piotr Dziwiński, Andrzej Przybył, Paweł Trippner, Józef Paszkowski, Yoichi Hayashi
    Journal of Artificial Intelligence and Soft Computing Research, 2021
    Over the last several decades, neuro-fuzzy systems (NFS) have been widely analyzed and described in the literature because of their many advantages. They can model the uncertainty characteristic of human reasoning and the possibility of a universal approximation. These properties allow, for example, for the implementation of nonlinear control and modeling systems of better quality than would be possible with the use of classical methods. However, according to the authors, the number of NFS applications deployed so far is not large enough. This is because the implementation of NFS on typical digital platforms, such as, for example, microcontrollers, has not led to sufficiently high performance. On the other hand, the world literature describes many cases of NFS hardware implementation in programmable gate arrays (FPGAs) offering sufficiently high performance. Unfortunately, the complexity and cost of such systems were so high that the solutions were not very successful. This paper proposes a method of the hardware implementation of MRBF-TS systems. Such systems are created by modifying a subclass of Takagi-Sugeno (TS) fuzzy-neural structures, i.e. the NFS group functionally equivalent to networks with radial basis functions (RBF). The structure of the MRBF-TS is designed to be well suited to the implementation on an FPGA. Thanks to this, it is possible to obtain both very high computing efficiency and high accuracy with relatively low consumption of hardware resources. This paper describes both, the method of implementing MRBFTS type structures on the FPGA and the method of designing such structures based on the population algorithm. The described solution allows for the implementation of control or modeling systems, the implementation of which was impossible so far due to technical or economic reasons.
  • A New Hybrid Particle Swarm Optimization and Genetic Algorithm Method Controlled by Fuzzy Logic
    Piotr Dziwinski, Lukasz Bartczuk
    IEEE Transactions on Fuzzy Systems, 2020
    The performance of the well-known particle swarm optimization (PSO) method can be improved by minimizing the possibility of premature convergence in a local minimum. We can achieve this by modifying some of the particles with crossover and mutation operators used in genetic algorithms. However, the impact of genetic operators on the optimization process should depend on the current state of the PSO algorithm. In this article, we propose to use the neuro-fuzzy system to dynamically determine the strength with which these operators will affect the process of finding the optimal solution. Results obtained for well-known benchmark functions demonstrate the advance of the proposed method over the original PSO algorithm and its selected modifications.
  • A New Auto Adaptive Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm
    Piotr Dziwiński, Łukasz Bartczuk, Józef Paszkowski
    Journal of Artificial Intelligence and Soft Computing Research, 2020
    The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.
  • Nonlinear Fuzzy Modelling of Dynamic Objects with Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm
    Łukasz Bartczuk, Piotr Dziwiński, Piotr Goetzen
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2020
  • A New Hybrid Particle Swarm Optimization and Evolutionary Algorithm
    Piotr Dziwiński, Łukasz Bartczuk, Piotr Goetzen
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2019
  • A population based algorithm and fuzzy decision trees for nonlinear modeling
    Piotr Dziwiński, Łukasz Bartczuk, Krzysztof Przybyszewski
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018
  • Symbolic regression with the AMSTA+GP in a non-linear modelling of dynamic objects
    Łukasz Bartczuk, Piotr Dziwiński, Andrzej Cader
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2018
  • A method for non-linear modelling based on the capabilities of PSO and GA algorithms
    Piotr Dziwiński, Łukasz Bartczuk, Huang Tingwen
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017
  • The concept on nonlinear modelling of dynamic objects based on state transition algorithm and genetic programming
    Łukasz Bartczuk, Piotr Dziwiński, Vladimir G. Red’ko
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2017
  • A new approach for using the fuzzy decision trees for the detection of the significant operating points in the nonlinear modeling
    Piotr Dziwiński, Eduard D. Avedyan
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2016
  • A new method of the intelligent modeling of the nonlinear dynamic objects with fuzzy detection of the operating points
    Piotr Dziwiński, Eduard D. Avedyan
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2016
  • A new approach to nonlinear modeling based on significant operating points detection
    Piotr Dziwiński, Eduard D. Avedyan
    Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science, 2015
  • A new algorithm for identification of significant operating points using swarm intelligence
    Piotr Dziwiński, Łukasz Bartczuk, Andrzej Przybył, Eduard D. Avedyan
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2014
  • Hybrid state variables - Fuzzy logic modelling of nonlinear objects
    Łukasz Bartczuk, Andrzej Przybył, Piotr Dziwiński
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2013
  • A new method for dealing with unbalanced linguistic term set
    Łukasz Bartczuk, Piotr Dziwiński, Janusz T. Starczewski
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
  • Fully controllable ant colony system for text data clustering
    Piotr Dziwiński, Łukasz Bartczuk, Janusz T. Starczewski
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
  • New linguistic hedges in construction of interval type-2 FLS
    Piotr Dziwiński, Janusz T. Starczewski, Łukasz Bartczuk
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2010
  • New method for generation type-2 fuzzy partition for FDT
    Łukasz Bartczuk, Piotr Dziwiński, Janusz T. Starczewski
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2010
  • Learning methods for type-2 FLS based on FCM
    Janusz T. Starczewski, Łukasz Bartczuk, Piotr Dziwiński, Antonino Marvuglia
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2010
  • Ant focused crawling algorithm
    Piotr Dziwiński, Danuta Rutkowska
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2008
  • Algorithm for generating fuzzy rules for WWW document classification
    Piotr Dziwiñski, Danuta Rutkowska
    Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2006