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Bioinspired Bare Bones Mayfly Algorithm for Large-Scale Spherical Minimum Spanning Tree.

Tian Zhang1,2, Yongquan Zhou1,2, Guo Zhou3

  • 1College of Artificial Intelligence, Guangxi University for Nationalities, Nanning, China.

Frontiers in Bioengineering and Biotechnology
|March 18, 2022
PubMed
Summary
This summary is machine-generated.

A new bare bones mayfly algorithm (BBMA) optimizes spherical minimum spanning tree (MST) problems. This parameter-free approach enhances convergence and accuracy, outperforming existing swarm intelligence algorithms.

Keywords:
Prüfer codebare bones mayfly algorithmbioinspired algorithmlarge-scale spherical MSTmayfly algorithm

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Combinatorial Optimization

Background:

  • The Mayfly Algorithm (MA) is a population-based, bio-inspired optimization technique.
  • MA's effectiveness is hindered by numerous parameters requiring complex tuning for diverse problems.
  • The Minimum Spanning Tree (MST) problem is a fundamental challenge in graph theory and network design.

Purpose of the Study:

  • To introduce a parameter-free variant of the Mayfly Algorithm, termed the Bare Bones Mayfly Algorithm (BBMA).
  • To develop a mathematical model for solving a spherical variant of the MST problem.
  • To evaluate BBMA's performance on large-scale spherical MST problems.

Main Methods:

  • Developed the Bare Bones Mayfly Algorithm (BBMA) incorporating Gaussian distribution and Lévy flight.
  • Formulated a mathematical model for the spherical Minimum Spanning Tree (MST) problem.
  • Applied BBMA to solve large-scale spherical MST instances and compared results with other swarm intelligence algorithms.

Main Results:

  • BBMA demonstrated improved convergence speed and solution accuracy compared to standard MA.
  • The algorithm effectively explored and exploited the search space for optimization.
  • BBMA significantly outperformed other swarm intelligence algorithms on sixteen scales of spherical MST problems.

Conclusions:

  • The proposed BBMA offers a robust, parameter-free alternative for optimization tasks.
  • BBMA is highly effective for solving large-scale spherical MST problems.
  • This research advances the application of bio-inspired algorithms in combinatorial optimization.