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

Area of Science:

• Computational Intelligence
• Swarm Intelligence
• Optimization Algorithms

Background:

• Population diversity decline and convergence issues hinder swarm intelligence optimizers in complex problems.
• The Red-billed Blue Magpie Optimizer (RBMO) faces challenges in exploration-exploitation balance and late-stage convergence.
• Existing algorithms struggle with efficiency in middle and later stages of optimization.

Purpose of the Study:

• To propose a multi-strategy enhanced variant of RBMO, named CLD-RBMO, to address its limitations.
• To improve global exploration, local refinement, and directed exploitation in RBMO.
• To enhance the performance and applicability of RBMO for complex optimization tasks.

Main Methods:

• Introduced a hierarchical perturbation mechanism (Logistic chaotic mapping and Lévy flight) for enhanced early-stage exploration.
• Employed a Cauchy-Gauss hybrid mutation operator for improved local optima escape.
• Incorporated a stochastic differential mutation strategy for directional guidance and accelerated convergence in later stages.

Main Results:

• CLD-RBMO showed significant superiority over the original RBMO and other swarm intelligence algorithms on CEC2017 benchmark functions.
• Demonstrated improved optimization accuracy, stability, and performance ranking.
• Validated dynamic performance improvements and statistical significance through convergence analysis and Wilcoxon rank-sum tests.

Conclusions:

• CLD-RBMO effectively overcomes the limitations of the original RBMO, particularly in complex optimization problems.
• The proposed multi-strategy enhancements lead to superior performance, stability, and generalization capabilities.
• The algorithm shows strong potential for application in mechanical engineering optimization and other complex domains.