Lévy-Enhanced Swarm Intelligence for Optimizing a Multiobjective Biofuel Supply Chain
Bibliographic record
Abstract
Engineering systems are currently plagued by various complexities and uncertainties. Metaheuristics have emerged as an essential tool for effective engineering design and operations. Nevertheless, conventional metaheuristics still struggle to reach optimality in the face of highly complex engineering problems. Aiming to further boost the performance of conventional metaheuristics, strategies such as hybridization and various enhancements have been added into the existing solution methods. In this work, swarm intelligence techniques were employed to solve the real-world, large-scale biofuel supply chain problem. Additionally, the supply chain problem considered in this chapter is multiobjective (MO) in nature. Comparative analysis was then performed on the swarm techniques. To further enhance the search capability of the best solution method (GSA), the Lévy flight component from the Cuckoo Search (CS) algorithm was incorporated into the Gravitational Search Algorithm (GSA) technique; developing the novel Lévy-GSA technique. Measurement metrics were then utilized to analyze the results.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".