An Enhanced Red-Billed Blue Magpie Optimizer Based on Superior Data Driven for Numerical Optimization Problems
Siyan Li, Lei Kou

TL;DR
This paper introduces an improved version of the Red-Billed Blue Magpie Optimizer to solve complex numerical optimization problems more effectively.
Contribution
The paper introduces an Enhanced RBMO with a two-stage covariance-driven strategy and a Powell mechanism to improve optimization performance.
Findings
ERBMO outperforms ten algorithms on the CEC 2017 benchmark suite across multiple dimensions.
The algorithm shows strong global exploration and local convergence accuracy.
ERBMO successfully solves practical engineering design problems with high-quality solutions.
Abstract
The Red-Billed Blue Magpie Optimizer (RBMO) is a recently introduced swarm-based meta-heuristic that has shown strong potential in engineering optimization but remains under-explored. To address its inherent limitations, this paper proposes an Enhanced RBMO (ERBMO) that synergistically incorporates two key strategies: a dominant-group-based two-stage covariance-driven strategy that captures evolutionary trends to improve population quality while reinforcing global exploration, and a Powell mechanism (PM) that eliminates dimensional stagnation and markedly strengthens convergence. Extensive experiments on the CEC 2017 benchmark suite demonstrate that ERBMO outperforms ten basic and improved algorithms in global exploration, local convergence accuracy and robustness, attaining Friedman ranks of 1.931, 1.621, 1.345 and 1.276 at 10D, 30D, 50D and 100D, respectively. Furthermore, empirical…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Vehicle Routing Optimization Methods · Advanced Multi-Objective Optimization Algorithms
