Analysis of Optimal Solutions of a Benchmark Water Distribution Network for Exploring the Global Optimality of the Current-best Solution
Bibliographic record
Abstract
The optimal design of water distribution networks (WDNs) has always attracted the attention of researchers due to the complexities involved. The general cost optimization problem is categorized as the NLP-Hard problem. Several evolutionary algorithms (EAs) that can explore the entire search from multiple starting points have been developed in the last three decades. The EAs have more chances of reaching the global optimal solution. However, whether these algorithms converge to a global optimal solution or not is always doubtful, as these algorithms do not guarantee the same solution in the two different runs. A two-source benchmark network is considered for which several better solutions have been reported by different researchers in the last 15 years. The available solutions of this network are analysed based on certain parameters, usually observed in the global optimal solution. The main objective of the study is to explore the possibility of achieving a better than the current best-solution, as the global optimality of the current best is not confirmed. Further, the proposed methodology is applied to test the current best solutions of two additional networks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".