A hybrid adaptive iterated local search heuristic for the maximal covering location problem
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Adaptive iterated local search (AILS) is a recently proposed metaheuristic paradigm that focuses on adapting the diversity control of iterated local search by online learning mechanisms. It has been successfully applied to the capacitated vehicle routing problem (CVRP) and the heterogeneous vehicle routing problem. Hybridizing it with path relinking (PR) has further improved the intensification of the method for the CVRP, providing outstanding results. However, the potential of this metaheuristic has not yet been investigated on other combinatorial optimization problems, such as location problems. In this paper, we develop a version of AILS for the maximal covering location problem (MCLP). This problem consists of locating a number of facilities to maximize the covered customer demand, where a given facility location can meet the demand of customers located within a coverage radius. Experiments on large‐scale instances of the MCLP indicate that AILS hybridized with PR, called AILS‐PR, outperforms the state‐of‐the‐art metaheuristic.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 it