A hybrid adaptive iterated local search heuristic for the maximal covering location problem
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.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".