Exploring Swarm Intelligence for Enhanced Clustering in Artificial Intelligence: A PSO-Kmeans Hybrid Approach
Bibliographic record
Abstract
In recent years, the integration of swarm intelligence-based metaheuristic optimization techniques into Artificial Intelligence (AI) has garnered significant attention.This project aims to investigate the potential applications of swarm intelligence techniques within the domain of AI.By leveraging the collective behavior and adaptive nature of swarm intelligence, these metaheuristic optimization methods offer unique opportunities for solving complex problems in AI.Numerous optimization methods have been proposed in academic research to address clustering-related challenges, but swarm intelligence has established a prominent position in the field.Particle swarm optimization (PSO) is the most popular swarm intelligence technique and one of the researchers' favorite areas.In this study, we introduce a novel clustering approach that integrates PSO with the K-means algorithm, aimed at enhancing clustering outcomes by effectively addressing common clustering challenges.The PSO algorithm has been shown to converge successfully during the initial stages of a global search, but around the global optimum.The proposed algorithm is designed to organize a given dataset into multiple clusters.To assess its effectiveness, we tested the algorithm on five different datasets.We then compared its clustering performance with that of the K-means and PSO algorithms, evaluating it based on metrics such as execution time, accuracy, quantization error, and both intra-cluster and inter-cluster distances.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".