Optimization of pump operations in a complex water supply network: new genetic algorithm frameworks
Bibliographic record
Abstract
In previous papers, a simple genetic algorithm (GA) was developed for the optimization of pump operations in water-distribution networks.Its application at the water supply network of Milano showed the possibility of a great improvement of its performance in terms of both energy and economic savings.In the present paper is now investigated the possibility of using different and improved GAs to obtain better results.Improvements concerned the description of the pump conditions with a real number (and therefore in continuous form) and the introduction of elitism and of a slightly modified form of mutation.Simulations were obviously performed with reference to the same model under the same assumptions of the previous papers.Results showed significant improvements in the passage from a discrete to a continuous description of the pumps functioning and a slight improvement using elitism and no differences using mutation.The latter result might need some more research: mutation is introduced to enlarge the space in which the 'individuals' perform their search, and there is the need to understand whether this little improvement is due to the poor performance of this mutation or instead, because the space of search is already well defined.The need for more in-depth investigations is also investigated in the present paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".