A parallel ant colony optimization algorithm for all-pair routing in MANETs
Bibliographic record
Abstract
A mobile ad hoc network (MANET) consists of mobile wireless nodes that communicate in a distributed fashion without any centralized administration. The nodes instantaneously and dynamically form a network on the fly when it is needed. We define an irregular application as one that changes the network dynamically during runtime, exhibits chaotic load balancing among the processors and unpredictable communication behavior among the nodes during runtime. An ad hoc network has all these characteristics and hence could be considered as an irregular application from the parallel computing perspective. In this paper, we design an on-demand routing algorithm called source update for MANET using a meta-heuristic based on the ant colony optimization (ACO) search technique. We develop a mechanism to detect cycles, parallelize this algorithm on a distributed memory machine using MPI, and study the performance of the parallel algorithm. On a distributed network of workstations, we obtain a relative speedup of 7 with 10 processors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".