CBWO: Chaotic Beluga Whale Optimizer for Numerical and Engineering Optimization Problems
Why this work is in the frame
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Bibliographic record
Abstract
Beluga Whale Optimization (BWO) is a recently developed meta-heuristics search algorithm to provide good balance between the exploration phase and the exploitation phase in solving benchmark optimization problems. However, the local search of the basic BWO algorithm has slow convergence rate due to its poor exploitation capability. We proposed a hybrid algorithm using a chaotic variant of the present optimization algorithm in order to enhance its exploitation ability and abbreviated as CBWO. To appraise the performance of CBWO, it is first verified on 23 standard benchmark functions. A comparative study has been done that shows the advantage of the proposed algorithm and associated with a number of existing algorithms. Simulation results were carried out on eleven classical engineering problems. Pseudo code of CBWO algorithm is presented in paper. Results come to know that CBWO could be more effective in optimization with quicker and advanced convergence rate and accuracy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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