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An improved opposition-based crow search algorithm for biodegradable material classification.

A M Al-Fakih1, Z Y Algamal2, M K Qasim3

  • 1Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor, Malaysia and Department of Chemistry, Faculty of Science, Sana'a University, Sana'a, Yemen.

SAR and QSAR in Environmental Research
|April 26, 2022
PubMed
Summary
This summary is machine-generated.

A new Opposite-Based Learning-Crow Search Algorithm (OBL-CSA) improves classification of biodegradable materials. This optimized model enhances prediction accuracy and reduces computational time compared to other algorithms.

Keywords:
Crow search algorithmQSBRbiodegradable materialsclassificationdescriptor selectionopposition-based learning

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Area of Science:

  • Chemometrics
  • Computational Chemistry
  • Machine Learning

Background:

  • Developing reliable quantitative structure-activity relationship (QSAR) models is essential in chemometrics.
  • Accurate classification of biodegradable materials requires efficient modeling techniques.

Purpose of the Study:

  • To propose an improved Crow Search Algorithm (CSA) using Opposite-Based Learning (OBL), termed OBL-CSA.
  • To enhance the exploration and exploitation capabilities of CSA for quantitative structure-biodegradation relationship (QSBR) modeling.
  • To classify biodegradable materials effectively.

Main Methods:

  • Adaptation of the Crow Search Algorithm (CSA) with Opposite-Based Learning (OBL).
  • Application of the OBL-CSA for quantitative structure-biodegradation relationship (QSBR) modeling.
  • Comparative analysis against standard CSA, Particle Swarm Algorithm (PSO), Black Hole Algorithm (BHA), Grey Wolf Algorithm (GWA), and Whale Optimization Algorithm (WOA).

Main Results:

  • The OBL-CSA demonstrated improved classification performance in QSBR modeling.
  • Reduced computational time was observed with OBL-CSA compared to other tested algorithms.
  • Enhanced exploration and exploitation capabilities of the CSA were achieved.

Conclusions:

  • OBL-CSA offers a valuable advancement for classifying biodegradable materials.
  • The proposed method provides a more efficient and accurate approach to QSBR modeling.
  • This optimization technique can be a significant resource in material science and environmental chemistry.