A Review of the Application of Swarm Intelligence-Based Algorithms for Optimization in Microgrids
Bibliographic record
Abstract
The unique features of swarm intelligence algorithms have led to their use in solving complex and diverse problems in various fields. These algorithms are widely used as a powerful tool in artificial intelligence and computational science. In this review, the capabilities of swarm intelligence-based algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), and fish swarm algorithm (FSA) for optimizing the performance of microgrids are examined. First, the concepts of microgrids and the introduction of each swarm intelligence-based algorithm are presented. Then, the advantages and disadvantages of the application of the algorithms are stated. The application of the algorithms in various topics such as energy management, protection, loss reduction, and virtual impedance in microgrids is stated. Finally, the existing challenges and future research directions are mentioned. This study can be a foundation for future research that uses the capabilities of swarm intelligence to solve real-world challenges in various fields of energy systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".