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Abstract

The artificial bee colony (ABC) algorithm is well known and widely used optimization method based on swarm intelligence, and it is inspired by the behavior of honeybees searching for a high amount of nectar from the flower. However, this algorithm has not been exploited sufficiently. This research paper proposes a novel method to analyze the exploration and exploitation of ABC. In ABC, the scout bee searches for a source of random food for exploitation. Along with random search, the scout bee is guided by a modified genetic algorithm approach to locate a food source with a high nectar value. The proposed algorithm is applied for the design of a nonlinear controller for a continuously stirred tank reactor (CSTR). The statistical analysis of the results confirms that the proposed modified hybrid artificial bee colony (HMABC) achieves consistently better performance than the traditional ABC algorithm. The results are compared with conventional ABC and nonlinear PID (NLPID) to show the superiority of the proposed algorithm. The performance of the HMABC algorithm-based controller is competitive with other state-of-the-art meta-heuristic algorithm-based controllers in the literature.
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Authors and Affiliations

Nedumal Pugazhenthi P
1
S. Selvaperumal
1
ORCID: ORCID
K. Vijayakumar
2

  1. Department of EEE, Syed Ammal Engineering College, Ramanathapuram, Tamilnadu, India
  2. Department of electronics and instrumentation, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu, India
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Abstract

Efforts of the scientific community led to the development of multiple screening approaches for COVID-19 that rely on machine learning methods. However, there is a lack of works showing how to tune the classification models used for such a task and what the tuning effect is in terms of various classification quality measures. Understanding the impact of classifier tuning on the results obtained will allow the users to apply the provided tools consciously. Therefore, using a given screening test they will be able to choose the threshold value characterising the classifier that gives, for example, an acceptable balance between sensitivity and specificity. The presented work introduces the optimisation approach and the resulting classifiers obtained for various quality threshold assumptions. As a result of the research, an online service was created that makes the obtained models available and enables the verification of various solutions for different threshold values on new data.
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Authors and Affiliations

Michał Kozielski
1
ORCID: ORCID
Joanna Henzel
1
ORCID: ORCID
Joanna Tobiasz
2
ORCID: ORCID
Aleksandra Gruca
1
Paweł Foszner
3
ORCID: ORCID
Joanna Zyla
2
ORCID: ORCID
Małgorzata Bach
4
Aleksandra Werner
4
ORCID: ORCID
Jerzy Jaroszewicz
5
Joanna Polańska
2
ORCID: ORCID
Marek Sikora
1
ORCID: ORCID

  1. Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
  2. Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
  3. Department of Graphics, Computer Vision and Digital Systems, Silesian University of Technology, Gliwice, Poland
  4. Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland
  5. Department of Infectious Diseases and Hepatology, Medical University of Silesia, Katowice, Poland

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