Layout and size optimization of tree-like pipe networks by incremental solution building ants
Bibliographic record
Abstract
Application of an ant colony optimization algorithm (ACOA) for simultaneous layout and size optimization of tree-like pipe networks is described in this paper using two different formulations. In the first formulation, each link of the base graph is considered as the decision point of the problem. Each decision point is considered in turn and the ants are then required to choose any of the available options at the current decision point. The list of available pipe diameters with the null option included for each link constitutes the available options in this formulation. In the second approach, the network nodes are considered as the decision points of the problem. The available options in this formulation are represented by the list of allowable pipe diameters for all plausible links such that the resulting network is a tree network. The plausible links at each decision point are provided by a tree-growing algorithm. This formulation leads to a very small search space compared with the first algorithm, as each ant is now forced to create a feasible solution regarding the layout geometry of the network. This approach fully exploits the sequential nature of the ACOA in building solutions, which is believed to be one of the main advantages of these algorithms compared with other general metaheuristics. The proposed methods are applied to find the optimal layout of two benchmark examples in the published literature and the results are presented and compared with the existing results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".