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  1. Home
  3. Many-objective African Vulture Optimization Algorithm: A Novel Approach For Many-objective Problems.
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  3. Many-objective African Vulture Optimization Algorithm: A Novel Approach For Many-objective Problems.

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Many-objective African vulture optimization algorithm: A novel approach for many-objective problems.

Heba Askr1,2, M A Farag2,3, Aboul Ella Hassanien2,4

  • 1Faculty of Computers and Artificial Intelligence, Information Systems Department, University of Sadat City, ‎Sadat City, Egypt.

Plos One
|May 17, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new many-objective African vulture optimization algorithm (MaAVOA) to solve complex optimization problems. MaAVOA enhances diversity and convergence, outperforming existing methods on benchmark functions and real-world engineering challenges.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Evolutionary Computation

Background:

  • Many-objective optimization problems (MaOPs) present challenges in balancing exploration and exploitation.
  • Existing algorithms struggle to effectively address the complexities of MaOPs.

Purpose of the Study:

  • To propose a novel many-objective African vulture optimization algorithm (MaAVOA) for solving MaOPs.
  • To enhance the diversity and convergence capabilities of optimization algorithms.

Main Methods:

  • Developed MaAVOA by simulating African vultures' foraging and navigation behaviors.
  • Introduced a social leader vulture and an alternative pool-based environmental selection mechanism.
  • Integrated the Fitness Assignment Method (FAM) for convergence and diversity, and Reproduction of Archive Solutions (RAS) to improve solution quality.

Main Results:

  • MaAVOA demonstrated superior performance over existing algorithms on DTLZ benchmark functions, measured by inverted generational distance and hypervolume.
  • The algorithm showed strong adaptation abilities in convergence and diversity.
  • MaAVOA successfully tackled real-world constrained engineering MaOPs, including series-parallel systems and gas turbine overspeed protection.

Conclusions:

  • MaAVOA is an effective algorithm for solving many-objective optimization problems.
  • The proposed algorithm offers promising solutions for decision-makers in complex engineering applications.
  • MaAVOA provides a robust approach to maintaining diversity and achieving convergence in MaOPs.