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This study introduces an improved Tree-Seed Algorithm (PDSTSA) for continuous optimization problems. PDSTSA enhances convergence speed and optimization accuracy, outperforming existing algorithms in simulations and real-world engineering tests.

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • Continuous optimization problems present challenges like local optima and slow convergence.
  • Existing Tree-Seed Algorithms (TSA) exhibit limitations in exploration and exploitation.
  • The need for robust algorithms to solve complex optimization tasks is critical.

Purpose of the Study:

  • To propose an enhanced Tree-Seed Algorithm (PDSTSA) addressing TSA limitations.
  • To improve global search capability and population diversity.
  • To validate the efficacy of PDSTSA on benchmark and engineering problems.

Main Methods:

  • Incorporation of a pattern search strategy for enhanced global detection.
  • Introduction of a dimension permutation mutation strategy to maintain population diversity.
  • Implementation of an elimination and update mechanism for iterative refinement.

Main Results:

  • PDSTSA demonstrated superior optimization accuracy and convergence speed compared to seven other algorithms on IEEE CEC2015 test functions.
  • Wilcoxon rank sum test confirmed statistically significant differences in performance.
  • PDSTSA proved effective and superior for engineering constrained optimization problems.

Conclusions:

  • The proposed PDSTSA effectively overcomes the limitations of traditional TSA.
  • PDSTSA offers a robust and efficient solution for continuous and constrained optimization.
  • The algorithm shows significant potential for practical applications in engineering and beyond.