How samuelmartinezupm/nsga-ii-based-satellite-beam-hopping-bh-algorithm works
This MATLAB research application generates LEO satellite cell/user scenarios, compares demand-based beam hopping with a hybrid NSGA-II optimizer, and evaluates illumination and resource-allocation solutions using UC, EC, and TTS. The documented workflow runs through MultiObjectiveFunction; the separate statistical-confidence tools analyze hypervolume results across configurations. Relationships are based on the README because no source excerpts were provided; GUI wiring and other undocumented connections are therefore omitted.
Main entry points: MultiObjectiveFunction.m Optimization workflow · NSGAII.m NSGA-II · loadSmall.m Small scenarios · loadLarge.m Large scenarios
Components 21
Scenario preparation
- Small scenarios
LEO/loadSmall.m - Large scenarios
LEO/loadLarge.m - Traffic distribution
LEO/Traffic_Distribution.m - Cell generation
LEO/Cell_Scenario.m - User model
LEO/u.m - Cell model
LEO/c.m
Beam-hopping optimization
- Optimization workflow
LEO/MultiObjectiveFunction.m - Demand initialization
LEO/DB_PopulationInitialization.m - Random initialization
LEO/PopulationInitialization.m - NSGA-II
Algorithms/Multi-objective optimization/NSGA-II/NSGAII.m - LEO mutation
LEO/MutationLEO.m - Demand local search
LEO/DB_LocalSearch.m - Demand mutation
LEO/DB_Mutation.m
Solution evaluation
- Solution evaluation
LEO/MultiObjectiveFunction.m - Optimization FoM
LEO/FOM_calculation_optimization_based.m - Constraint checks
LEO/Single_Objective_Constraints.m - Demand-based FoM
FOM_calculation_demand_based_band_slots.m
Results analysis
- HV generation
Statistical_Confidence/HV_files_generation.m - Confidence analysis
Statistical_Confidence/HV_statistic_confidence.m - Statistical tests
Statistical_Confidence/matlab/statistics.m
Other components
- Researcher
Connections 21
- Researcher → Optimization workflow: runs
- Optimization workflow → Small scenarios: generates instance
- Optimization workflow → Large scenarios: generates instance
- Small scenarios → Traffic distribution: generates users
- Large scenarios → Traffic distribution: generates users
- Small scenarios → Cell generation: generates cells
- Large scenarios → Cell generation: generates cells
- Traffic distribution → User model: creates users
- Cell generation → Cell model: creates cells
- Optimization workflow → Demand initialization: initializes population
- Optimization workflow → Random initialization: initializes population
- Optimization workflow → NSGA-II: runs optimization
- Optimization workflow → Demand-based FoM: runs comparison
- NSGA-II → Solution evaluation: evaluates solutions
- Solution evaluation → Optimization FoM: calculates FoM
- Solution evaluation → Constraint checks: checks constraints
- NSGA-II → LEO mutation: mutates solutions
- NSGA-II → Demand local search: applies local search
- NSGA-II → Demand mutation: applies mutation
- HV generation → Confidence analysis: provides HV samples
- Confidence analysis → Statistical tests: runs tests