Comparison of Three Evolutionary Algorithms: PSOA, ACOA and BCOA on Recognition Arabic Characters Problem
Bibliographic record
Abstract
Intelligence techniques such as Particle swarm optimization, Genetic algorithm, Ant colony optimization , Bee Colony Optimization can apply on the system as classification method for better result This paper we survey three techniques Ant Colony Optimization Algorithm (ACOA), Particle Swarm Optimization Algorithm (PSOA), and Bee Colony Optimization Algorithm (BCOA),their algorithm and reason to use. In recent years, the area of Evolutionary Computation has come into these three. Three of the popular developed approaches are Particle Swarm Optimization Algorithm (PSOA), Ant Colony Optimization Algorithm (ACOA) and Bee Colony Optimization Algorithm (BCOA), are used in optimization problems. Since the three approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their implementation. The problem area chosen is that recognition of Final forms of Arabic handwritten characters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".