Rebirthing particle swarm optimization algorithm: application to storm water network design
Bibliographic record
Abstract
Stochastic search methods, such as the particle swarm optimization (PSO) algorithm, are primarily directed by two main features — exploration and exploitation. Exploration is the ability of the algorithm to broadly search through the solution space for new quality solutions, whereas exploitation is responsible for refining the search in the neighborhood of the good solutions found previously. Proper balance between these features is sought, to obtain good performance of these algorithms. An explorative mechanism is introduced in this paper to improve the performance of the PSO algorithm. The method is based on introducing artificial exploration into the algorithm by randomly repositioning the particles approaching stationary status. A velocity measure is used to distinguish between flying and stationary particles. This can be sought as a sudden death followed by a rebirth of these particles. Two options are tested for the rebirthing mechanism, which are (i) clearing and (ii) keeping the memory of rebirthing particles. The global best particle is exempted from rebirthing process so that the most useful of the swarm’s past experiences is not lost. The method is applied to a benchmark storm water network design problem and the results are presented and compared with those of the original algorithm and other methods. The proposed method, though simple, is shown to be very effective in avoiding local optima, leading to an improved version of the algorithm at no extra computational effort.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".