An Insight Review on Jellyfish Optimization Algorithm and Its Application in Engineering
Bibliographic record
Abstract
Present paper conducts the study on metaheuristic jellyfish optimization algorithm and reviews work done on it. This is a newly developed meta-heuristic optimization algorithm concentrates on movements of jellyfish in ocean such as search of food in sea current, motion inside swarm, bloom, etc. The optimization demonstrates higher and additional favourable results versus other bio-inspired optimization algorithms. Some of the work is discussed in this paper are: finding of unknown parameters of Photo Voltic models using premature convergence strategy, finding of optimal VAR coordination in Automated Delivery System, estimating the parameters of single-phase power transformers, overcoming the drawbacks and improving the quality of network service, tracking the overall maximum power point under partial shade conditions, improving MapReduce job performance and increasing resource usage in Hadoop yarn, solving engineering problems optimally with multiple objectives, dimensionally reducing humanoid application feature, solving the problems to identify the parameters of Polymer Exchange Membrane Fuel Cell models, watermark extraction process and preserving location confidentiality to avoid congestion with less guaranteed delay, OPNN network planning to categorise plant diseases, Brain image segmentation method and multiple radio access network. All the above work reviewed shows promising results from the algorithm. More work can be conducted using this algorithm in future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".