Design optimization of orbital angular momentum fibers using the gray wolf optimizer
Bibliographic record
Abstract
Optical data communication based on the orbital angular momentum (OAM) of light is a recently proposed method to enhance the transmission capacity of optical fibers. This requires a new type of optical fiber, the main part of the optical communication system, to be designed. Typically, these fibers have a ring-shaped refractive index profile. We aim to find an optimized cross section refractive index profile for an OAM fiber in which the number of supported OAM modes (channels), mode purity, and the effective refractive index separation of OAM modes to other fibers modes are maximized. However, the complexity of the relationship between structural parameters and optical transmission properties of these fibers has resulted in the lack of a comprehensive analytical method to design them. In this paper, we investigate the process of designing OAM fibers and propose a framework to design such fibers by using artificial intelligence optimizers. It is worth mentioning here that this problem is intrinsically a multiobjective optimization problem, and the actual solution for such problems is not unique and leads to a set of optimum solutions. Therefore, at the end of the optimization process, a wide range of optimal designs will be obtained in which a trade-off is established in each of the solutions. We solve this problem with the multiobjective gray wolf optimizer (GWO) and compare the results with that of the single-objective GWO. The framework can easily find many optimal designs that support more than 20 OAM modes. The obtained results show that the proposed method is comprehensive and can optimize the structure of any OAM fibers. No human involvement, simplicity, and being straightforward are the main advantages of the proposed framework.
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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".