MOSSA: An Efficient Swarm Intelligent Algorithm to Solve Global Optimization and Carbon Fiber Drawing Process Problems
Bibliographic record
Abstract
In this article, the sparrow search algorithm (SSA) is extended to the multiobjective SSA (MOSSA) with the purpose of efficiently solving the multiobjective optimization problems (MOPs). First, the MOSSA adaptively evaluates nondominated sparrow individuals stored in the external archive (EA) by using an adaptive mesh approach, which is utilized to obtain the best producer. Second, the scrounger sparrows adjust their trajectories according to the location of the best producer, called the scrounger follow strategy, which can improve the quality of the solutions when solving MOPs. Then, the proposed scouter search strategy is capable of maintaining population diversity and accelerate convergence. Moreover, the EA is pruned with the aim of avoiding the waste of computing resources. Extensive experiments with 22 benchmark examples validate the effectiveness of our approach against six state-of-the-art optimization approaches. Finally, the MOSSA is applied in the carbon fiber drawing process problems and the stretching parameters obtained by the MOSSA is reasonable.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".