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Enhanced crayfish optimization algorithm: Orthogonal refracted opposition-based learning for robotic arm trajectory

Yuefeng Leng1, Chunlai Cui1, Zhichao Jiang1

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The Enhanced Crayfish Optimization Algorithm (ECOA) improves robotic arm trajectory planning by enhancing global search and avoiding local optima. This metaheuristic approach offers superior performance and stability for complex optimization tasks.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Optimization

Background:

  • Trajectory planning in high-dimensional spaces is computationally complex.
  • Metaheuristic (MH) algorithms offer practical solutions.
  • The Crayfish Optimization Algorithm (COA) has limitations in global search and local optima convergence.

Purpose of the Study:

  • To propose an Enhanced Crayfish Optimization Algorithm (ECOA) for robotic arm trajectory planning.
  • To improve global search capability and convergence stability.
  • To enhance solution quality and search efficiency.

Main Methods:

  • Incorporated a tent chaotic map for population initialization.
  • Replaced traditional step size adjustment with a nonlinear perturbation factor.
  • Implemented an orthogonal refracted opposition-based learning strategy.

Main Results:

  • ECOA demonstrated superior performance in trajectory planning experiments.
  • Reduced costs by 15% compared to the best competing algorithm and 10% over COA.
  • Showcased improved solution quality, robustness, and convergence stability.

Conclusions:

  • ECOA effectively addresses limitations of the original COA.
  • The proposed strategies enhance optimization performance.
  • ECOA shows potential for various engineering optimization challenges.