Ant colony optimization with distributed colonies for dynamic environments on multiple GPUs
Bibliographic record
Abstract
Dynamic environments pose many challenges as the search space is irregular, un- structured, with the data and problem space changing over time. The algorithms executing on these environments should adapt to the varying dynamic conditions. In this research we consider Ant Colony Optimization algorithm (ACO), a technique inspired by real ants in nature and therefore, should be adaptable to dynamic environ- ments. However, some studies in the literature show the contrary. Population-based ACO was introduced, a hybrid technique that combines concepts from Genetic Algo- rithms for solving problems in dynamic environments. In this thesis, we argue and show that ACO is as good as PACO or even better in some cases, by incorporating lo- cal search techniques to exploit the search space, tuning parameters in the algorithm to explore the search space and, using migration between multiple colonies (or island model) for convergence. The multiple colonies are implemented on multiple GPUs for efficiency. We perform various experiments on a dynamic travelling salesperson dataset and compare ACO and PACO with local search and island model. We also show that the parameter tuning has a significant influence on the accuracy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".