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Opposition-based learning techniques in metaheuristics: classification, comparison, and convergence analysis.

Rihab Lakbichi1, Farouq Zitouni1, Saad Harous2

  • 1Department of Computer Science and Information Technology, University of Kasdi Merbah, Laboratory of Artificial Intelligence and Information Technology, Ouargla, Algeria.

Peerj. Computer Science
|September 24, 2025
PubMed
Summary
This summary is machine-generated.

Opposition-based learning (OBL) enhances metaheuristic algorithms (MAs). Quasi-reflection OBL demonstrated superior convergence speed and solution quality in tested MAs, outperforming other OBL variants.

Keywords:
Metaheuristic algorithmsOBL variantsOpposition-based learningOptimization

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristic Computing

Background:

  • Opposition-based learning (OBL) is a key strategy for improving metaheuristic algorithms (MAs).
  • A structured analysis of OBL variants' impact on MA performance is lacking.
  • Existing MAs often suffer from slow convergence, limited exploration, and exploration-exploitation imbalance.

Purpose of the Study:

  • To categorize and analyze nine distinct OBL techniques.
  • To systematically assess the effectiveness of five OBL variants integrated into five MAs.
  • To demonstrate OBL's capability to enhance MAs facing common optimization challenges.

Main Methods:

  • Implemented five OBL variants within differential evolution, genetic algorithm, particle swarm optimization, artificial bee colony, and harmony search.
  • Evaluated hybridized algorithms across initialization and generation update phases.
  • Tested algorithms on 12 benchmark functions from the CEC2022 suite, analyzing key performance metrics and using a Friedman test for statistical validation.

Main Results:

  • Quasi-reflection opposition-based learning consistently outperformed other OBL variants.
  • The enhanced MAs showed improved convergence speed and solution quality across most benchmark functions.
  • Statistical validation confirmed significant performance differences among OBL-enhanced MA variants.

Conclusions:

  • Opposition-based learning significantly enhances metaheuristic algorithm performance.
  • Quasi-reflection OBL is a highly effective variant for improving convergence and solution quality.
  • This study provides a structured framework for understanding and applying OBL in metaheuristic optimization.