Convergence analysis of the tabu-based real-coded small-world optimization algorithm
Bibliographic record
Abstract
A novel, tabu-based real-coded small-world optimization algorithm (TR-SWA) is proposed. Tabu search is adopted to avoid duplicate searches of the real-coded small-world optimization algorithm (R-SWA). A crossover operator is introduced to construct search operators. The convergence behaviour of this TR-SWA scheme is shown by establishing the Markov model, and it is proved that TR-SWA meets the convergence theorem of a general random search algorithm proposed by Solis and Wets. Simultaneously, martingale convergence theorems are used to prove the nearly universal strong convergence of TR-SWA. Finally, five benchmark functions are introduced to evaluate the performance of TR-SWA: comparisons are made between TR-SWA, particle swarm optimization, binary-coded small-world optimization algorithm and R-SWA. Numerical experiments demonstrate that the addition of the tabu search improves the performance of R-SWA for most of the investigated optimization problems, and the global convergence of TR-SWA is guaranteed if the feasible set is bounded.
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 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 | 0.003 | 0.007 |
| 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 | 0.001 |
| 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".