Research on Majorization Algorithm and Application of Geotechnical Engineering Structure Based on Granule Swarm Majorization
Bibliographic record
Abstract
The traditional majorization design method makes too many restrictions on the objective function to be optimized and its constraints, which brings a lot of inconvenience to solving majorization problems in practical projects. A new stochastic Optimization algorithm based on swarm intelligence - granule swarm algorithm (PSO) was put forward in, and it has been widely concerned by researchers. When the algorithm is initialized, the granules are randomly divided into several sub-granule groups, each sub-granule group evolves independently according to a given strategy, and random migration and adaptive mutation of granules are carried out in the specified period of evolution to maintain the diversity of the entire population., to avoid premature convergence. The inverse analysis of geotechnical engineering majorization is essentially a typical majorization problem of complex nonlinear functions. The use of global majorization algorithm is an ideal way to solve this problem. low productivity. The new algorithm is applied to the elastic-plastic parameter inversion of geotechnical materials. The results show that compared with the conventional granule swarm majorization algorithm, the improved algorithm significantly improves the search efficiency of parameters, and the results that meet the accuracy requirements can be obtained with less iterations, which reduces the amount of calculation of elastic- plastic back analysis of geotechnical engineering. It is a feasible parameter inversion method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".