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Related Experiment Video

Updated: Aug 15, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A self-adaptive quantum equilibrium optimizer with artificial bee colony for feature selection.

Changting Zhong1, Gang Li2, Zeng Meng3

  • 1Department of Engineering Mechanics, State Key Laboratory of Structural Analyses for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China; School of Civil Engineering and Architecture, Hainan University, Haikou 570228, China.

Computers in Biology and Medicine
|January 6, 2023
PubMed
Summary
This summary is machine-generated.

A new method, self-adaptive quantum equilibrium optimizer with artificial bee colony (SQEOABC), improves feature selection (FS) for machine learning. This approach enhances classification accuracy by optimizing feature subsets, outperforming existing algorithms on benchmark and real-world COVID-19 data.

Keywords:
Artificial bee colonyEquilibrium optimizerFeatures selectionMetaheuristicQuantum theory

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Area of Science:

  • Computer Science
  • Machine Learning
  • Optimization

Background:

  • Feature selection (FS) is crucial for machine learning, aiming to identify optimal feature subsets to enhance classification accuracy.
  • Combinatorial optimization challenges in FS necessitate powerful optimization algorithms.
  • Equilibrium Optimizer (EO), a physics-based metaheuristic, shows promise but can suffer from premature or local convergence in FS tasks.

Purpose of the Study:

  • To introduce a novel hybrid metaheuristic algorithm, SQEOABC (Self-Adaptive Quantum Equilibrium Optimizer with Artificial Bee Colony), for effective feature selection.
  • To address the convergence limitations of the standard EO in feature selection applications.
  • To improve the performance and robustness of feature selection methods in machine learning.

Main Methods:

  • Developed SQEOABC by integrating quantum theory and a self-adaptive mechanism into the EO's updating rules to enhance convergence speed and stability.
  • Incorporated the updating mechanism from the Artificial Bee Colony (ABC) algorithm to refine feature subset selection and improve solution quality.
  • Validated SQEOABC on 25 benchmark datasets from the UCI repository and a real-world COVID-19 dataset.

Main Results:

  • SQEOABC demonstrated superior performance compared to several state-of-the-art metaheuristic algorithms and variants of the Equilibrium Optimizer.
  • Statistical analysis of fitness values and classification accuracy confirmed the effectiveness of the proposed SQEOABC algorithm.
  • The algorithm achieved better results in terms of both optimization performance and classification accuracy across diverse datasets.

Conclusions:

  • The proposed SQEOABC algorithm effectively overcomes the convergence issues of the standard EO for feature selection.
  • SQEOABC offers a robust and superior approach for identifying optimal feature subsets, leading to improved classification accuracy.
  • The algorithm's effectiveness is validated on both benchmark and real-world datasets, highlighting its practical applicability.