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Snake Optimization Algorithm Augmented by Adaptive t-Distribution Mixed Mutation and Its Application in Energy

Yinggao Yue1, Li Cao1, Changzu Chen1

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Summary
This summary is machine-generated.

This study introduces an enhanced snake optimization algorithm using adaptive t-distribution mixed mutation. The improved method achieves faster convergence and higher accuracy, outperforming traditional techniques.

Keywords:
adaptive t-distribution mutationengineering application problemsreverse learningsnake optimization algorithmtent chaotic map

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • Traditional snake optimization suffers from random initialization, slow convergence, and low accuracy.
  • Addressing these limitations is crucial for improving optimization performance.

Purpose of the Study:

  • To propose an adaptive t-distribution mixed mutation snake optimization strategy.
  • To enhance the performance of the snake optimization algorithm by improving initialization, convergence speed, and accuracy.

Main Methods:

  • Utilized Tent-based chaotic mapping and quasi-reverse learning for population initialization.
  • Introduced an adaptive t-distribution mixed mutation foraging strategy for enhanced exploration.
  • Replaced the mating mode with an opposite-sex attraction mechanism for improved global search.

Main Results:

  • The enhanced snake optimization algorithm demonstrates accelerated convergence and improved solution accuracy.
  • The proposed method shows superior robustness and accuracy compared to the standard snake optimization technique.
  • The integrated improvements synergistically enhance the algorithm's overall performance.

Conclusions:

  • The adaptive t-distribution mixed mutation snake optimization strategy effectively overcomes the drawbacks of the traditional method.
  • The enhanced algorithm achieves a better balance between local and global exploitation capabilities.
  • This improved optimization technique offers a more robust and accurate solution for complex problems.