An enhanced dung beetle optimization algorithm based-on multi-strategies for solving global optimization problems
Why this work is in the frame
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Bibliographic record
Abstract
The Dung Beetle Optimization (DBO) algorithm exhibits rapid convergence and robust search capabilities, yet its performance is constrained by excessive reliance on global best and worst solutions. To resolve these weaknesses, this paper introduces an enhanced DBO that incorporates multiple strategies, named DCWDBO. The dynamic opposition-based learning mechanism improves the quality of the initial population. Horizontal and vertical crossover strategies are incorporated to strengthen search capabilities. To preserve high population diversity throughout iterations, the original boundary-control mechanism is replaced with rules from the Wave Search Algorithm. To evaluate DCWDBO’s effectiveness, it was compared with PSO, SCA, SCSO, and standard DBO using benchmark functions from CEC 2017, 2020, and 2022. Results indicate that DCWDBO achieves reliable performance, demonstrating robust global exploration, stable convergence, and superior large-scale optimization capability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it