Exploring Common Patterns in Well-Known Metaheuristic Optimization Algorithms
Bibliographic record
Abstract
Considering the wide range of problems in various fields of science and engineering researchers always think of finding possible real-world solutions to improve the quality of people’s lives. Metaheuristic algorithms are optimization techniques that can discover desirable solutions to complex problems in a reasonable time. According to previous studies, approximately 540 Metaheuristic Algorithms have been introduced, more than 350 of which appeared in the last decade. The emergence of various metaheuristic algorithms has grown significantly in recent years and must be fully investigated. Due to the introduction of their variant models in recent years, the issue of basic similarities among algorithms with different names has expanded. This raises a fundamental question: Can a mathematical equation be proposed as a general template covering several similar main algorithms by applying minor changes in its variables or parameters? In this study, we aim to provide a general mathematical formulation that can help us to understand the algorithms better and improve them more easily, which will reduce redundancy, and improve the parameter settings, in some cases, algorithms may need unique formulations to address distinct challenges effectively.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".