Optimal Multi-Objective Design of an Integrated INS/AHRS System in GPS-Denied Environments Using the Real-coded Memetic Algorithm
Bibliographic record
Abstract
A novel meta-heuristic optimization algorithm is developed to solve multi-objective Multi-disciplinary Design Optimization (MDO) problems.The new algorithm, called multi-objective adaptive real-coded Memetic Algorithm (MARCOMA), is suitable for large scale optimization problems.MARCOMA is then applied to solve an MDO problem.The problem has many design variables and three disciplines including Navigation and guidance.Each discipline has its own design variables and analysis codes.Pitch Programming is used as the guidance law.A three-channel autopilot is used for stabilization during the separation phase and for executing guidance commands of the Aerial-Launched Vehicle (ALV) during flight phase.Navigation discipline has an inertial navigation system and attitude and heading reference system to estimate Euler angles at GPS-denied environment.For this purpose, Extended Kalman Filter parameters is optimized by measuring of angles to cooperate ALV to orbit.All disciplines are integrated in a 6-DOF flight simulation and the two objectives, elevation angle estimation and inverse of injection velocity, are minimized concurrently.The result is a Pareto set of non-dominated solutions within the performance space.The designer can choose an optimal solution based on his/her preferences and compromises.The examination of different initial condition scenarios shows the excellent performance of the optimization algorithm in solving the large-scale problem.
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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 | 0.001 |
| 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".