A levy flight based strategy to improve the exploitation capability of arithmetic optimization algorithm for engineering global optimization problems
Bibliographic record
Abstract
Abstract The existing arithmetic optimization algorithm is a meta‐heuristics algorithm that utilizes distribution behaviors for the different parameters in mathematics. The different mathematical operator like division, subtraction, addition, and multiplication holds the inherent capability to explore global maxima and minima. In the proposed research, levy flight‐based improved arithmetic optimization algorithm has been proposed for better optimal solutions to various engineering design problems. The fundamental arithmetic optimization algorithm's local search is slow and has a slow convergence rate due to its weak exploitation capacity. In the proposed work, the exploration and exploitation phase of the existing arithmetic optimization algorithm has been enhanced using the levy flight mechanism. In order to validate the effectiveness of the proposed optimizer, the improved algorithm has been tested for 23 standard benchmark problems and 10 real‐life engineering design problems. The proposed algorithm has been compared with other classical algorithms like biogeography based optimization algorithm, arithmetic optimization algorithm, moth‐flame optimization algorithm, genetic algorithm, flower pollination algorithm, particle swarm optimization, gray wolf optimization algorithm, BAT algorithm, chi‐square algorithm, firefly algorithm, gravitational search algorithm, and differential evolution algorithm. The obtained result reveals that the proposed hybrid levy flight arithmetic optimization algorithm performs best on the number of test functions including engineering design problems with excellent fitness value and excellent convergence. This article is helpful to improve the exploitation capability of arithmetic optimization algorithms for engineering global optimization problems.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".