An Enhanced Red-Billed Blue Magpie Optimizer Based on Superior Data Driven for Numerical Optimization Problems.

Area of Science:

• Computational Intelligence
• Optimization Algorithms
• Swarm Intelligence

Background:

• The Red-Billed Blue Magpie Optimizer (RBMO) is a novel swarm-based meta-heuristic with potential in engineering optimization.
• Existing RBMO methods have limitations that hinder their full potential.

Purpose of the Study:

• To introduce an Enhanced Red-Billed Blue Magpie Optimizer (ERBMO).
• To address the limitations of the original RBMO algorithm.
• To improve global exploration and convergence accuracy.

Main Methods:

• Incorporation of a dominant-group-based two-stage covariance-driven strategy for enhanced population quality and exploration.
• Integration of a Powell mechanism (PM) to mitigate dimensional stagnation and improve convergence.
• Extensive testing on the CEC 2017 benchmark suite and practical engineering design problems.

Main Results:

• ERBMO demonstrated superior performance compared to ten other algorithms across various dimensions (10D, 30D, 50D, 100D).
• Achieved excellent Friedman ranks, indicating high global exploration and local convergence accuracy.
• Consistently delivered high-quality solutions for real-world constrained optimization tasks.

Conclusions:

• ERBMO effectively overcomes the limitations of the original RBMO.
• The proposed enhancements significantly improve exploration and convergence capabilities.
• ERBMO shows broad applicability and potential for real-world engineering optimization problems.