Metaheuristic Optimization of Artificial Neural Networks: A Comprehensive Survey of Techniques, Taxonomies, and Trends (2015–2025)
Bibliographic record
Abstract
Artificial Neural Networks (ANNs) excel across vision, language, and decision-making, yet their performance hinges on well-chosen weights, hyperparameters, and architecture settings where classical gradient methods can stall or overfit.This survey consolidates a decade of work (2015-2025) on metaheuristic assistance for ANN optimization, covering evolutionary, swarm-intelligence, physics-inspired, and hybrid paradigms.We propose a unified taxonomy that cross-classifies optimization targets (weights, structure, hyperparameters) with hybridization depth (sequential, embedded, post-training), and we synthesize quantitative trends from recent mappings alongside a curated dataset.The evidence indicates a sharp post-2019 acceleration, with swarm methods remaining the largest family and hybrids the fastest-growing, particularly in energy, industrial, healthcare, and cybersecurity applications.We analyze methodological gaps statistical rigor, compute/energy reporting, and reproducibility and outline a research agenda centered on self-adaptive controllers, multi-objective and constraint-aware formulations, and quantum-inspired diversity mechanisms.By integrating taxonomy, original visuals, and critical appraisal, this article clarifies how metaheuristics act as adaptive schedulers for modern ANN training and provides practical guidance for designing robust, resourceaware optimization pipelines.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".