AR-RBMO: An enhanced red-billed blue magpie optimizer with attraction-repulsion and dynamic balancing strategies for global optimization
Bibliographic record
Abstract
Abstract Metaheuristic algorithms have been extensively applied to real-world optimization problems because of their flexibility and strong problem-solving ability. However, as optimization problems become increasingly complex and diverse, stand-alone algorithms encounter inherent limitations that diminish their effectiveness. The red-billed blue magpie optimizer (RBMO), a relatively new swarm intelligence algorithm, has demonstrated significant potential, while its performance remains limited by restricted global exploration capability a tendency to converge prematurely to local optima. Combining the strengths of multiple algorithms enables the creation of more effective hybrid optimization methods. Building on this idea, this study introduces an attraction–repulsion enhanced red-billed blue magpie optimizer (AR-RBMO). The algorithm incorporates an attraction–repulsion mechanism to improve global search, a best-solution attraction strategy to direct the population towards high-quality regions, an escape strategy to avoid local optima, and a dynamic exploration–exploitation balance strategy based on optimal solution feedback. Systematic experiments on the CEC2017 benchmark suite, covering 30-, 50-, and 100-dimensional functions, evaluate AR-RBMO against 18 representative metaheuristic algorithms. Results from the Friedman test indicate average rankings of 2.133, 1.75, and 1.4667, respectively, confirming AR-RBMO’s overall superiority. The Wilcoxon rank test further validates that these performance improvements are statistically significant. Evaluation on six classical engineering optimization problems yields high-quality solutions, demonstrating robust global search capabilities, high convergence accuracy, and consistent solution stability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".