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Differential evolution and particle swarm optimization against COVID-19.

Adam P Piotrowski1, Agnieszka E Piotrowska2

  • 1Institute of Geophysics, Polish Academy of Sciences, Ks. Janusza 64, 01-452 Warsaw, Poland.

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|August 24, 2021
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Summary

Differential Evolution (DE) and Particle Swarm Optimization (PSO) were widely applied to COVID-19 research in 2020, primarily for epidemiological models and image classification. However, studies often lacked methodological detail and favored older algorithm versions.

Keywords:
ApplicationsCOVID-19Differential evolutionEvolutionary computationParticle swarm optimizationSwarm intelligence

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Area of Science:

  • Computational Intelligence
  • Bioinformatics
  • Epidemiology

Background:

  • The COVID-19 pandemic spurred rapid scientific advancements and the application of optimization techniques.
  • Differential Evolution (DE) and Particle Swarm Optimization (PSO) are established metaheuristics with broad scientific applications.

Purpose of the Study:

  • To survey the applications of DE and PSO in COVID-19 research published in 2020.
  • To analyze the effectiveness and popularity of DE and PSO compared to other metaheuristics in the context of the pandemic.
  • To provide insights for both practitioners and metaheuristic experts.

Main Methods:

  • Literature review of COVID-19 related studies published in 2020 that utilized DE or PSO.
  • Analysis of application domains, methodological reporting, algorithm variants used, and reasons for method selection.
  • Comparative assessment of DE and PSO against other metaheuristics.

Main Results:

  • DE and PSO were predominantly used for calibrating epidemiological models and for image-based classification of patients/symptoms.
  • Methodological reporting for DE and PSO was often insufficient, with choices not always optimized for the problem.
  • Predominantly, basic, older variants of DE and PSO were applied, neglecting recent advancements.
  • Algorithm citation counts and code availability were key factors in metaheuristic selection.

Conclusions:

  • DE and PSO are popular choices for specific COVID-19 computational tasks, but their application could be improved with more rigorous methodological reporting and utilization of advanced variants.
  • The selection of metaheuristics is influenced by practical factors like existing research and code accessibility, rather than solely by algorithmic suitability.
  • Future research could benefit from exploring more recent DE and PSO advancements and ensuring comprehensive methodological descriptions.