A Hybrid Iterated Local Search Algorithm for the Global Planning Problem of Survivable 4G Wireless Networks
Bibliographic record
Abstract
In this paper, we propose a hybrid iterated local search (ILS) heuristic, named GPP4G-ILS, to solve the global planning problem of survivable wireless networks. The planning problem of wireless networks is to determine a set of sites among potential sites to install the various network devices in order to cover a given geographical area. It should also make the connections between the devices in accordance with well-defined constraints. The global planning consists in solving this problem without dividing it into several subproblems. The objective is to minimize the cost of the network while maximizing its survivability. The GPP4G-ILS algorithm is a new form of hybridization between the ILS algorithm and the integer linear programing (ILP) method. We propose a configuration that allows to reuse a previously developed ILP algorithm by integrating it in the ILS algorithm. This allows to benefit from the advantages of both methods. The ILS algorithm is used to effectively explore the search space, while the ILP algorithm is used to intensify the solutions obtained. The performance of the algorithm was evaluated using an exact method that generates optimal solutions for small instances. For larger instances, lower bounds have been calculated using a relaxation of the problem. The results show that the proposed algorithm is able to reach solutions that are, on average, within 0.06% of the optimal solutions and 2.43% from the lower bounds for the instances that cannot be solved optimally, within a reduced computation time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".