An Improved Red-Billed Blue Magpie Algorithm and Its Application to Constrained Optimization Problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• Conventional Red-Billed Blue Magpie Optimization (RBMO) struggles with premature convergence in high-dimensional, constrained problems.
• Over-reliance on population mean vectors limits RBMO's effectiveness.

Purpose of the Study:

• To introduce an Improved Red-Billed Blue Magpie Optimization (IRBMO) algorithm.
• To enhance RBMO's performance on complex optimization tasks through a multi-strategy fusion framework.

Main Methods:

• IRBMO integrates Logistic-Tent chaotic mapping for enhanced population diversity.
• A dynamic balance factor coordinates global and local search.
• A dual-mode perturbation mechanism combines Jacobi curve and Lévy flight strategies.

Main Results:

• IRBMO demonstrated statistically significant improvements in robustness, convergence accuracy, and speed.
• Outperformed 16 algorithms on CEC-2017 and CEC-2022 benchmark suites.
• Successfully applied to constrained engineering design problems and 3D UAV path planning, outperforming 15 alternatives.

Conclusions:

• IRBMO provides an efficient and robust solution for high-dimensional constrained optimization problems.
• The proposed multi-strategy fusion framework effectively addresses RBMO's limitations.
• IRBMO shows strong potential for real-world applications like path planning.