AI-Driven and Bio-Inspired Algorithms for Network Optimization: A Review of Applications, Challenges, and Case Studies
Bibliographic record
Abstract
Despite the complexity that modern network infrastructures such as IoT, $5 \mathrm{G} / 6 \mathrm{G}$ and smart cities have grown to become, much more advanced optimization strategies are needed. Adaptability and scalability are typically problematic for traditional congestion control and network management, causing a poor allocation of bandwidth, the potential for poor cybersecurity management, and control of traffic. Detailed review of AI driven and bio inspired algorithms in network optimization, intrusions detection, energy efficient routing, clustering and congestion control is done in this literature review. Reinforcement learning, deep learning as well as fuzzy logic-based model driven by AI has improved algorithms of traffic forecasting, latency reduction, adaptive network management. Moreover, Particle Swarm Optimization (PSO), ACO, Grey Wolf Optimizer (GWO), Salp Swarm Optimization (SSO), etc. bio inspired algorithms have been demonstrated efficient for use in intrusion detection systems (IDS), routing optimization, energy efficient clustering. The testing of AI controlled multi source congestion control, reinforcement learning based TCP congestion management and fuzzy logicbased congestion detection was also conducted, case studies which proved to reduce the latency by 57%, prevent packet loss 45%, and increase utilization bandwidth by 24%. Nevertheless, these advances are still plagued by problems of computational complexity, dependency on some dataset, and applicability to the real world. The review also explains the need for hybrid AI models, edge-based intelligence, and scalable bio inspired frameworks to improve performance. They suggest that AI and bio inspired approaches have transformative solution to the next generation networks which enable optimizing traffic control, cyber security and energy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".