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Improved Sparrow Algorithm Based on Game Predatory Mechanism and Suicide Mechanism.

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This study enhances the sparrow search algorithm with game predatory and suicide mechanisms to avoid local optimization traps while maintaining fast convergence. The improved algorithm demonstrates superior performance in UAV path planning and other optimization tasks.

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Area of Science:

  • Optimization Algorithms
  • Artificial Intelligence
  • Computational Intelligence

Background:

  • The sparrow search algorithm (SSA) exhibits rapid convergence but is prone to local optima.
  • Existing diversity metrics may not account for boundary violations in search processes.

Purpose of the Study:

  • To enhance the sparrow search algorithm by introducing novel mechanisms to improve its global search capability.
  • To refine diversity evaluation by incorporating a measure for invalid boundary searches.
  • To validate the improved algorithm's effectiveness in complex optimization problems, including UAV path planning.

Main Methods:

  • Integration of game predatory and suicide mechanisms into the SSA.
  • Population initialization using the good point set strategy.
  • Development of a new index to measure invalid boundary searches for improved diversity assessment.

Main Results:

  • The enhanced SSA effectively avoids local optimization while retaining fast convergence.
  • Improved population initialization and diversity metrics lead to more robust search performance.
  • The algorithm shows strong optimization capabilities and competitiveness against other methods in benchmark tests and UAV path planning.

Conclusions:

  • The proposed modifications significantly improve the sparrow search algorithm's global optimization performance.
  • The enhanced algorithm offers a competitive and effective solution for UAV path planning and other complex optimization challenges.