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Adaptive and migration-enhanced tree seed algorithm for multi-threshold CT image segmentation and lung cancer

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|January 16, 2026
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Summary
This summary is machine-generated.

The Adaptive and Migration-enhanced Tree Seed Algorithm (AMTSA) improves upon the original Tree Seed Algorithm (TSA) for complex optimization tasks. AMTSA demonstrates superior performance in high-dimensional problems and medical image analysis.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Swarm Intelligence

Background:

  • The original Tree Seed Algorithm (TSA) suffers from premature convergence and local optima in high-dimensional optimization.
  • Existing TSA variants have limitations in addressing complex, large-scale problems effectively.

Purpose of the Study:

  • To introduce an Adaptive and Migration-enhanced Tree Seed Algorithm (AMTSA) to overcome the limitations of the standard TSA.
  • To enhance global exploration, adaptability, and convergence stability in complex optimization tasks.

Main Methods:

  • Implemented an adaptive tree migration mechanism for dynamic step-size and direction adjustment based on individual fitness.
  • Introduced an adaptive seed generation strategy using the dynamic Weibull distribution for flexible search control.
  • Incorporated a nonlinear step-size adjustment function inspired by the GBO algorithm to improve convergence stability.

Main Results:

  • AMTSA outperformed state-of-the-art optimizers (JADE, LSHADE) and TSA variants (STSA, fb-TSA, MTSA) on IEEE CEC 2014 benchmark functions.
  • In high-dimensional tests (30, 50, 100 dimensions), AMTSA achieved the best optimization results and fastest convergence.
  • An AMTSA-SVM model achieved 89.5% accuracy in lung cancer CT image segmentation, surpassing other methods.

Conclusions:

  • The proposed AMTSA effectively addresses TSA's deficiencies through adaptive migration, dynamic seed generation, and nonlinear step-size control.
  • AMTSA offers a more efficient and robust solution for high-dimensional and complex optimization problems.
  • The enhanced algorithm shows significant potential in real-world applications like medical image analysis.