Developed Swarm Optimizer: A New Method for Sizing Optimization of Water Distribution Systems
Bibliographic record
Abstract
AbstractThe introduction of metaheuristic algorithms in water resources engineering has greatly raised the need for continued development of appropriate optimization methodologies for analysis, planning, design, and operation of water resources systems. This paper proposes a novel developed swarm-based optimization algorithm named DSO, which integrates the accelerated particle swarm optimization (PSO) with the big bang-big crunch algorithm (BB-BC) to optimize the design of water distribution systems (WDSs). Traditional PSO is easy to fall into stagnation when no particle explores a position that is better than its previous best position for several iterations. To deal with the problem of maintaining diversity within the swarm and to enhance the exploration in the search, the concepts of the Big Crunch and Big Bang strategies from the BB-BC algorithm are incorporated into the global and local searching steps of the accelerated PSO, respectively. In addition, a harmony search–based strategy is used to contr...
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".