IIR Filter Design Using Multiobjective Artificial Bee Colony Algorithm
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Bibliographic record
Abstract
In this paper, IIR filters are designed using Gbest-guided Multiobjective Artificial Bee Colony algorithm (GMOABC). Artificial Bee Colony algorithm (ABC) is a stochastic optimization algorithm inspired by the food seeking behavior of honey bee colonies. Even though ABC algorithm can converge to a global optimum for complex problems, the time taken for convergence is longer than classical methods. Gbest guided multiobjective ABC algorithm can reduce the time taken for converging to global optimum and improves the quality of the solutions by tuning the search process towards the global best in each iteration. IIR filter design is a non-convex optimization problem and requires the optimization of both the magnitude and group delay. The results show that, GMOABC can achieve lower passband error and group delay error than the recent results obtained by other methods.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it