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

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.