Pipeline optimization using a novel hybrid algorithm combining front projection and the non-dominated sorting genetic algorithm-II (FP-NSGA-II)
Bibliographic record
Abstract
In this paper, a procedure for minimizing the pumping power, the number of pumping stations and the total pipeline mass required for a pipeline project is presented using multiobjective optimization. Two and three-objective optimization cases were considered. The decision variables included the outer diameter, wall thickness, suction pressure and discharge pressure. A novel hybrid multiobjective optimization algorithm combining NSGA-II with a simple front prediction (FP-NSGA-II) is proposed to improve upon the performance NSGA-II. Then, the application of the proposed algorithm to a problem taken from the open literature is presented and analyzed. The resulting Pareto domain was ranked using a cost function. Results indicate that FP-NSGA-II improved significantly convergence, spread and number of non-dominated solutions for the determination of the optimal design for a specified pipeline problem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".