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Adaptive dynamic crayfish algorithm with multi-enhanced strategy for global high-dimensional optimization and

Mohamed Elhosseny1,2, Mahmoud Abdel-Salam3, Ibrahim M El-Hasnony2

  • 1College of Computing and Informatics, University of Sharjah, Sharjah, UAE.

Scientific Reports
|March 28, 2025
PubMed
Summary
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The Adaptive Dynamic Crayfish Optimization Algorithm (AD-COA-L) enhances convergence speed and avoids local optima. This novel approach improves optimization performance in complex problems.

Area of Science:

  • Computational Intelligence
  • Metaheuristic Optimization
  • Algorithm Development

Background:

  • The Crayfish Optimization Algorithm (COA) faces challenges with slow convergence and local optima.
  • Existing metaheuristic algorithms often struggle with balancing exploration and exploitation.

Purpose of the Study:

  • To introduce an improved COA variant, Adaptive Dynamic COA with a Locally enhanced escape operator (AD-COA-L).
  • To address the limitations of poor convergence speed and local optimum convergence in the original COA.

Main Methods:

  • Utilizing Bernoulli map initialization for a high-quality, evenly distributed initial population.
  • Applying Adaptive Lens Opposition-Based Learning (ALOBL) to escape local optima and enhance solution quality.
  • Incorporating a local escape operator (LEO) to promote information sharing and prevent isolated solutions.
Keywords:
AdaptiveCrayfishEngineering problemsInertia weightLocal escape operator

Related Experiment Videos

  • Introducing a novel inertia weight to balance exploration and exploitation capabilities.

    • Main Results:

    • AD-COA-L demonstrated superior accuracy and balanced exploration-exploitation compared to 18 other algorithms on 29 CEC2017 benchmark functions.
    • The algorithm showed improved convergence speed across various dimensions (50 and 100).
    • AD-COA-L proved effective in solving seven complex real-world engineering optimization problems.

    Conclusions:

    • AD-COA-L significantly outperforms existing algorithms in terms of accuracy, convergence, and solution quality.
    • The proposed enhancements effectively mitigate local optima convergence and improve overall optimization performance.
    • AD-COA-L presents a competitive and advantageous metaheuristic for diverse optimization challenges.