Comparison of the Genetic Algorithm and the Particle Swarm Optimization for Minimizing Fuel Consumption for UAVs
Bibliographic record
Abstract
This paper compares the efficiency of two metaheuristics, namely the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO), for the problem of fuel minimization for uncrewed aerial vehicles (UAVs). When flying search-and-rescue or surveillance missions, it is critical to minimize fuel consumption in order to increase the autonomy of the vehicles. The method proposed consists of using the metaheuristic to compute optimized power settings for the UAV along the trajectory. The approach involves path smoothing in 3D using circular arcs for fixed-wing UAVs. Equations of motion and Newton's equation are used to calculate fuel consumption based on power settings. The metaheuristics are used to compute optimized power settings while adhering to physical constraints. The method is parallelized on a multicore processor for faster computation and optimization in case of trajectory changes. Results show that both the GA and the PSO are efficient at minimizing fuel consumption for the UAV with the PSO being slightly superior. The proposed method can reduce fuel consumption by up to 15% and the parallel implementation is 13.8× faster than a sequential implementation on the CPU.
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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".