Nonlinear threshold accepting meta-heuristic for combinatorial optimisation problems
Bibliographic record
Abstract
Local search algorithms are a wide class of improvement algorithms that start from a given solution and try to find a better solution in a defined neighbourhood of the current solution. Stochastic hill climbing algorithms, such as threshold accepting algorithms and simulated annealing are optimisation techniques which belong to the family of local search algorithms. The main difference between the existing algorithms resides in the mechanism of accepting or rejecting the candidate solution from the neighbourhood. In this paper, we test a simple but effective modification of the conventional threshold accepting algorithm. In the proposed variant, the acceptance rule is nonlinear. This acceptance rule is inspired from the RC-filter which is a low-pass filter used in electronics to reduce the amplitude of signals with higher frequencies. We apply the newly developed meta-heuristic to difficult instances of four combinatorial optimisation problems, namely quadratic assignment problem, dynamic discrete facility layout problems, flow-shop scheduling and berth allocation. The results presented show that this algorithm performs well for several test problems. Therefore, it can be used as a specialised heuristic for these problems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".