A New Algorithm for Water Distribution System Optimization: Discrete Dynamically Dimensioned Search
Bibliographic record
Abstract
The Dynamically Dimensioned Search (DDS) continuous global optimization algorithm by Tolson and Shoemaker (2007) is modified to solve discrete, single-objective, constrained Water Distribution System (WDS) design problems. The new algorithm is called Discrete Dynamically Dimensioned Search (DDDS). DDDS characteristics parallel those of DDS, namely that it is a simple, parsimonious and efficient global optimization algorithm. This paper evaluates DDDS in relation to Ant Colony Optimization (ACO) and Genetic Algorithms (GAs) for WDS optimization. The first implementation of DDDS, called DDDS-v1, was developed and then applied to the Hanoi (HP) and New York Tunnels (NYTP) benchmark WDS optimization problems without algorithm parameter-tuning and with a simple parameter-free penalty function approach. DDDS-v1 results are good for the NYTP in comparison with published ACO and GA results. DDDS-v1 identified the best known solution to the NYTP in 5/20 optimization trials. For HP, DDDS-v1 generated better average results than any ACO and GA results available from a previous study. Importantly, DDDS-v1 had no trouble finding the feasible region and returned final solutions from this region that were on average improved relative to other algorithms. Overall, findings suggest that DDDS shows good potential as a new tool for WDS optimization.
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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 | 0.000 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".