UAV path planning in presence of occlusions as noisy combinatorial multi-objective optimisation
Bibliographic record
Abstract
A realistic noisy combinatorial problem on surveillance by unmanned aerial vehicle (UAV) in presence of weather factors is defined. The presence of cloud coverage is considered as a posterior Gaussian noise in the visibility region of the UAV. Recent studies indicate that recombination-based search mechanisms are helpful in solving noisy combinatorial problems. The search strategy of univariate marginal distribution algorithm that includes only selection and recombination, which has a close association with genepool crossover, proves to be beneficial in solving constrained and multi-objective combinatorial problems in presence of noise. This paper proposes a solution methodology based on multi-objective UMDA (moUMDA) with diversification mechanisms for the multi-objective problem of UAV surveillance. To obtain a well-spread set of Pareto optimal solutions, relevant diversification mechanisms are important. Numerical simulations show that moUMDA with and without K-means clustering provides better quality solutions and a more diverse Pareto optimal set than NSGA-II in solving this noisy problem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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) | 0.001 | 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".