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

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 studies on practical engineering design problems confirm the algorithm’s capability to consistently deliver high-quality solutions, highlighting its broad applicability to real-world constrained optimization tasks. In future work, we will deploy the algorithm for real-world tasks such as UAV path-planning and resource-scheduling problems.

Figures