An Improved Red-Billed Blue Magpie Optimization for Function Optimization and Engineering Problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Metaheuristics

Background:

• The Red-Billed Blue Magpie Optimization (RBMO) algorithm is a novel metaheuristic.
• RBMO faces challenges including premature convergence and imbalanced exploration-exploitation.
• These limitations hinder its effectiveness in complex optimization tasks.

Purpose of the Study:

• To introduce an Improved Red-Billed Blue Magpie Optimization (IRBMO) algorithm.
• To address the inherent limitations of the original RBMO algorithm.
• To enhance performance in function optimization and engineering problems.

Main Methods:

• Integration of three synergistic strategies within a multi-population cooperative framework.
• Enhanced RBMO search with elite guidance for accelerated convergence.
• Adaptive differential evolution operator for improved local search and escaping local optima.
• Quasi-opposition-based learning for boosting global exploration and population diversity.

Main Results:

• Rigorous evaluation on 26 classical benchmark functions and real-world engineering design problems.
• IRBMO demonstrated significantly superior performance compared to the original RBMO.
• IRBMO outperformed other leading optimization algorithms in solution accuracy, convergence speed, and stability.

Conclusions:

• The proposed IRBMO algorithm effectively overcomes the limitations of the original RBMO.
• IRBMO offers enhanced capabilities for solving complex optimization problems.
• The synergistic strategies contribute to improved performance metrics and robustness.