Harmony Search Algorithm for Solving Combinatorial Optimization Problems: Bibliometric Analysis
Bibliographic record
Abstract
The Harmony Search Algorithm (HSA) is a nature-inspired algorithm that emulates the improvisational process of musicians and has been successfully applied to various optimization problems across diverse domains. While numerous studies have reviewed and surveyed the HSA, to the best of our knowledge, no bibliometric analysis of the algorithm's applications in the context of Combinatorial Optimization Problems (COPs) has been conducted within the Scopus database prior to this research. This study aims to provide a comprehensive bibliometric analysis of HSA applications in COPs by examining a total of 2134 articles. The descriptive and bibliometric analyses focused on identifying the most productive journals, leading researchers, highly cited articles, prolific countries in HSA research, and potential future directions. The results indicate that the Advances in Intelligent Systems and Computing journal has published 93 articles, accounting for 4.358% of the total publications. Geem emerged as a prominent figure in the field, with 88 documents and 11,489 citations since 2001, as determined using the RStudio software. In terms of country-wise contributions, China ranked first, producing 592 HSA-related documents. This analysis offers valuable insights for researchers and practitioners engaged in HSA applications within the realm of COPs.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.069 | 0.094 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".