Ant Colony with Stochastic Local Search for the Quadratic Assignment Problem
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Bibliographic record
Abstract
The existing ant colony optimization (ACO) algorithms for the quadratic assignment problem (QAP) are often combined with two kinds of stochastic local search (SLS) methods: the 2-opt local search and the tabu local search. In this paper, these two SLS methods are respectively improved according to the properties of ACO and QAP. For the 2-opt local search, a new random walk strategy is used to avoid a quick stagnation into local optima. Moreover, a forward-looking strategy is proposed to explore the neighborhood more thoroughly. In the case of tabu local search, a random walk strategy is also employed to avoid getting stuck at local optima. Experimental evaluation of the ACO algorithms combined with the improved local search proposed in this paper are conducted on problems from the well known QAPLIB library. The results demonstrate that each ACO algorithm, combined with its respective improved local search, has a better performance, in terms of the quality of the solution returned, than the ACO algorithm with the original local search techniques. Moreover, we also noticed that the improved methods outperform each other for different classes of problems
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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