Multi-Strategy Improved Red-Billed Blue Magpie Optimization Algorithm and Its Applications.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristic Computing

Background:

• The Red-billed Blue Magpie Optimization Algorithm (RBMO) faces challenges with convergence accuracy, population diversity, and local optima.
• Existing optimization algorithms often struggle to balance exploration and exploitation effectively.

Purpose of the Study:

• To propose a novel multi-strategy improved Red-billed Blue Magpie Optimization Algorithm (SWRBMO).
• To enhance the global exploration, convergence speed, local optima avoidance, and accuracy of the RBMO algorithm.
• To validate the effectiveness of SWRBMO on benchmark functions and engineering design problems.

Main Methods:

• Implemented an adaptive T-distribution-based sinh-cosh search strategy for improved global exploration and convergence.
• Introduced a neighborhood-guided reinforcement strategy to mitigate local optima.
• Incorporated a crossover strategy to boost convergence accuracy.
• Evaluated SWRBMO on CEC2005, CEC2019, and CEC2021 benchmark suites, including ablation studies.
• Performed statistical analysis using the Wilcoxon rank-sum test and validated on engineering design problems.

Main Results:

• SWRBMO exhibited faster convergence and higher accuracy compared to RBMO and other algorithms on CEC2019 and CEC2021 test suites.
• Ablation studies confirmed the contribution of each proposed strategy.
• SWRBMO achieved significant performance improvements on engineering design problems, outperforming other methods by substantial margins (up to nearly 100%).

Conclusions:

• The proposed SWRBMO effectively addresses the limitations of the original RBMO algorithm.
• SWRBMO demonstrates robust performance, improved accuracy, and strong potential for practical engineering applications.
• The multi-strategy approach enhances both the theoretical underpinnings and practical utility of the optimization algorithm.