A Survey of Ant Colony Optimization Algorithms for Telecommunication Networks
Why this work is in the frame
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Bibliographic record
Abstract
Optical and ad-hoc networks which fulfill the communications requirements of complex applications must meet the Quality of Service (QoS) demanded by these applications, such as transmission delay. These demands are hard to satisfy in the presence of unpredictable behavior in the environment such as interference, traffic congestion, etc. Algorithms based on Ant Colony Optimization (ACO) offer an effective approach to meet such challenges since they are well suited to the dynamic routing optimization and dynamic resource reassignment required by these applications. In this paper, the author presents a survey of Ant Colony Optimization variants applied to ad-hoc and optical networks. The ACO variant called AntHocNet in particular will be reviewed, analyzed, and criticized from the point of view of emergent applications for environment management such as Intelligent Transportation Systems (ITS).
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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