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Hybrid Multi-Objective Chameleon Optimization Algorithm Based on Multi-Strategy Fusion and Its Applications.

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This study introduces an improved chameleon optimization algorithm (ICSA) that enhances convergence speed and robustness. The enhanced algorithm demonstrates superior performance in UAV path planning, generating shorter and more stable paths.

Keywords:
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Area of Science:

  • Computational Intelligence
  • Swarm Intelligence
  • Optimization Algorithms

Background:

  • Chameleon Swarm Algorithm (CSA) suffers from slow convergence, poor robustness, and susceptibility to local optima.
  • Existing swarm intelligence algorithms require improvements in exploration and exploitation balance.

Purpose of the Study:

  • To propose a multi-strategy improved chameleon optimization algorithm (ICSA) addressing CSA's limitations.
  • To enhance the global search ability, optimization accuracy, and robustness of the chameleon optimization algorithm.

Main Methods:

  • Introduced logistic mapping for improved initial population diversity.
  • Implemented sub-population spiral search and Lévy flight with cosine adaptive weight for enhanced prey searching.
  • Incorporated nonlinear varying weight and refraction reverse-learning for improved prey capture and local optimum avoidance.

Main Results:

  • ICSA demonstrated superior convergence performance and optimization ability compared to five other swarm intelligent optimization algorithms on the CEC2005 benchmark test set.
  • Independent runs confirmed ICSA's enhanced performance metrics.
  • Application to UAV path planning resulted in shorter and more stable paths across various terrain scenarios.

Conclusions:

  • The proposed ICSA effectively overcomes the limitations of the standard CSA.
  • ICSA offers a robust and efficient optimization approach for complex problems, including UAV path planning.