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A Hybrid Nonlinear Greater Cane Rat Algorithm with Sine-Cosine Algorithm for Global Optimization and Constrained

Jinzhong Zhang1, Anqi Jin2, Tan Zhang1

  • 1School of Electrical and Photoelectronic Engineering, West Anhui University, Lu'an 237012, China.

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This summary is machine-generated.

The novel Sine-Cosine Greater Cane Rat Algorithm (SCGCRA) enhances swarm intelligence by improving solution accuracy and adaptability. This algorithm addresses limitations of the basic GCRA, offering superior performance in complex optimization tasks.

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benchmark functionsengineering designsexploration and exploitationgreater cane rat algorithmnonlinear strategysine–cosine algorithm

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Area of Science:

  • Computational Intelligence
  • Swarm Intelligence Algorithms
  • Optimization Techniques

Background:

  • The Greater Cane Rat Algorithm (GCRA) is inspired by animal foraging behavior but suffers from parameter sensitivity, low accuracy, and poor adaptability.
  • Existing swarm intelligence algorithms often struggle with balancing exploration and exploitation, leading to premature convergence or stagnation.

Purpose of the Study:

  • To introduce a hybrid Sine-Cosine Greater Cane Rat Algorithm (SCGCRA) to overcome the limitations of the basic GCRA.
  • To enhance search efficiency, accuracy, and adaptability in solving complex optimization problems.
  • To balance exploration and exploitation for identifying globally optimal solutions.

Main Methods:

  • A hybrid approach combining the Sine-Cosine Algorithm (SCA) with the Greater Cane Rat Algorithm (GCRA).
  • Incorporation of nonlinear control strategies and periodic oscillatory fluctuations from SCA to regulate search dynamics.
  • Testing the SCGCRA on 23 benchmark functions and 6 constrained engineering design problems.

Main Results:

  • The SCGCRA demonstrated superior performance compared to the basic GCRA across various optimization tasks.
  • Achieved faster convergence speeds, higher solution accuracy, and improved robustness.
  • Showcased enhanced population diversity and adaptability, effectively avoiding local optima and stagnation.

Conclusions:

  • The proposed SCGCRA effectively addresses the drawbacks of the basic GCRA, offering improved efficiency and accuracy.
  • The hybrid approach provides a synergistic effect, enhancing the algorithm's ability to solve complex engineering and benchmark problems.
  • SCGCRA exhibits significant potential for real-world optimization applications requiring robust and accurate solutions.