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An enhanced opposition-based African vulture optimizer for solving engineering design problems and global

Henry Blankson1, Vanisree Chandran1, Himadri Lala2

  • 1Department of Mathematics, SAS, VIT University, Vellore, Tamil Nadu, 632014, India.

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|September 26, 2025
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Summary
This summary is machine-generated.

This study introduces the Enhanced Opposition-based African Vulture Optimizer (EOBAVO), improving optimization by accelerating convergence and escaping local optima. EOBAVO offers an efficient solution for complex optimization challenges.

Keywords:
African vulture optimizerEnhanced opposition-based learningMetaheuristicRandom opposition-based learning

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • Conventional African Vulture Optimization (AVO) faces limitations in rough search spaces, requiring excessive iterations.
  • Escaping local optima and accelerating convergence are critical challenges in metaheuristic optimization.

Purpose of the Study:

  • To enhance the African Vulture Optimization algorithm using an improved opposition-based learning technique.
  • To develop a novel optimization algorithm, the Enhanced Opposition-based African Vulture Optimizer (EOBAVO), for improved performance.

Main Methods:

  • Integration of enhanced opposition-based learning (EOBL) with the African Vulture Optimization (AVO) algorithm.
  • Experimental validation using CEC2005 and CEC2022 benchmark functions and seven engineering problems.
  • Statistical analysis using t-tests and Wilcoxon rank-sum tests to compare performance.

Main Results:

  • The proposed EOBAVO demonstrated superior performance compared to existing leading optimization algorithms.
  • EOBAVO effectively accelerates convergence and aids in escaping local optima.
  • Statistical tests confirmed the significant outperformance of EOBAVO.

Conclusions:

  • The Enhanced Opposition-based African Vulture Optimizer (EOBAVO) is a competent and efficient algorithm for complex optimization tasks.
  • The integration of EOBL significantly improves the capabilities of the AVO algorithm.
  • EOBAVO presents a promising advancement in the field of computational intelligence and optimization.