Coupling a chaotically encoded firefly algorithm with ranking to a physics-based mathematical model for robust optimisation of a gas turbine energy system
Why this work is in the frame
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Bibliographic record
Abstract
The aim of this study is to probe the potentials of a well-known metaheuristic approach called firefly algorithm with ranking (FAR) for optimising the operating parameters of a complex gas turbine energy system. FAR is a modified version of classic firefly algorithm (FA) which is suited for handling complex constraint optimisation problems. Firstly, by using the first law of thermodynamics, a mathematical model is implemented to analyse the most important design parameters affecting the efficiency of the gas turbine energy system. Thereafter, two well-known chaotic maps, i.e., Gauss and sinusoidal maps, are embedded into the algorithmic structure of FAR to prepare a powerful tool for the considered problem. To ascertain the veracity and the efficacy of the proposed chaos-enhanced FAR (CFAR), a number of chaos-enhanced rival modern optimisers, i.e., chaotic artificial bee colony (CABC), chaotic particle swarm optimisation (CPSO), and chaotic genetic algorithm (CGA), are applied to the considered optimisation problem. The results indicate that CFAR can easily outperform the rival techniques, and yield robust and accurate results.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it