Shortest Path Algorithm Based Cheerleading Formation Design and Movement Coordination Optimization in Colleges and Universities under Multi-Intelligent Body Environment
Bibliographic record
Abstract
Cheerleading events are flourishing in China, the level of competition is rising, the number of competition groups and programs is increasing, the competition is becoming more and more intense, and the innovative research on formation design is an inevitable demand for the development trend of cheerleading.The study designed a multi-objective path planning model based on the intensity of willingness and consultation strategy, so that college cheerleading can avoid conflicts and reach the goal point of cheerleaders in the complex environment.Then an improved multiobjective particle swarm algorithm (MOPSO-CA) based on metacellular automata is proposed and applied to college cheerleading formations to realize the design of college cheerleading formations.The simulation results show that the MOPSO-CA algorithm can re-select the optimal movement direction angle according to the real-time positions of the moving obstacles and moving targets, which illustrates the effectiveness of the algorithm.Secondly the feasibility of the formation design conditions are suggested as: keeping the originality of the movement, the use of the moving route of the formation and the space of the venue, and the type of formation change.
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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 | 0.001 | 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".