A Hybrid Metaheuristic for High-Dimensional Constrained Optimization: Applications to Logistics and UAV Path...

Area of Science:

• Computational Intelligence
• Bio-inspired Computing
• Optimization Algorithms

Background:

• The Pied Kingfisher Optimizer (PKO) shows promise but has limitations in population distribution, historical data use, and local optima avoidance.
• Existing optimization methods struggle with complex, high-dimensional, and constrained engineering problems.

Purpose of the Study:

• To propose an Improved Pied Kingfisher Optimizer (IPKO) addressing PKO's limitations.
• To enhance exploration-exploitation balance and constraint adaptability in optimization algorithms.

Main Methods:

• Developed IPKO with four novel mechanisms: sine chaotic opposition-based initialization, adaptive directional search, stochastic perturbation-based information fusion, and centroid-based adaptive boundary handling.
• Evaluated IPKO on the CEC2017 benchmark suite and real-world engineering problems (cold chain logistics, dynamic multi-UAV path planning).

Main Results:

• IPKO demonstrated superior performance over eleven state-of-the-art algorithms on benchmark functions and engineering problems.
• Statistical significance confirmed via Friedman test and Holm's post-hoc procedure.
• Ablation studies validated the effectiveness of individual IPKO strategies.

Conclusions:

• IPKO is a robust and effective bio-inspired optimization approach for complex, high-dimensional, constrained engineering problems.
• IPKO offers improved solution quality, robustness, and constraint-handling capabilities.