Tourism Route Design of Characteristic Local Landscape Based on Improved Ant Algorithm
Bibliographic record
Abstract
With the rise of leisure tourism and the improvement of attention to rural landscape, the rural areas in the suburbs of metropolis absorb urban resources in the process of development, and have the advantage of developing rural tourism. The rural areas have been greatly improved in environmental infrastructure and landscape construction, and the rural areas in the suburbs have gradually become the consumption space of emerging urban residents. This paper improves the basic ant colony algorithm, adds the stage of centralized search scope, real-time updating pheromone, and pheromone rollback mechanism, and uses MATLAB software to customize a comprehensive travel route plan. In order to spend less money and get the most comfortable travel experience, the cost target and experience target are integrated, and a tourism route planning model based on ant colony algorithm is established, and the improved ant colony algorithm is used to solve the planning model. Finally, the model is applied to practical cases, and the optimal travel path that meets the requirements is obtained through calculation and analysis.
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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".