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Related Experiment Video

Updated: Aug 31, 2025

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Dynamic elite strategy mayfly algorithm.

Qianhang Du1, Honghao Zhu1

  • 1School of Computer and Information Engineering, Bengbu University, Bengbu, Anhui 233030, China.

Plos One
|August 25, 2022
PubMed
Summary
This summary is machine-generated.

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The improved mayfly algorithm (MA) enhances convergence speed and avoids local optima. This dynamic elite strategy (DESMA) boosts optimization performance for complex problems.

Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • The mayfly algorithm (MA) is a novel intelligent optimization technique.
  • MA suffers from premature convergence and susceptibility to local optima.

Purpose of the Study:

  • To introduce a dynamic elite strategy for the mayfly algorithm (DESMA).
  • To improve the convergence speed and accuracy of the mayfly algorithm.

Main Methods:

  • DESMA dynamically adjusts the search radius around the best mayfly.
  • Elite mayflies are generated within the determined search space.
  • A new elite mayfly replaces the current best if its fitness is superior.

Main Results:

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Last Updated: Aug 31, 2025

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  • DESMA demonstrated superior performance across 28 standard benchmark functions (CEC2013).
  • The improved algorithm shows significant gains in accuracy, speed, and stability compared to peers.

    • Conclusions:

    • The dynamic elite strategy effectively enhances the mayfly algorithm's optimization capabilities.
    • DESMA offers a robust solution for overcoming local optima and slow convergence in intelligent optimization.