Discrete Particle Swarm Optimization Algorithm for Dynamic Constraint Satisfaction with Minimal Perturbation
Bibliographic record
Abstract
Constraint Satisfaction Problems (CSPs) provide an appropriate framework to formulate many real-world applications including scheduling, planning and resource allocation. However, the CSP description can change due to the evolving environment. The latter points to the fact that constraints might be subject to change over time and this can affect the feasibility of the solutions found so far. These changes can be captured with the Dynamic CSP (DCSP) formalism that has been proposed and investigated in the literature. More formally, a DCSP can be seen as a series of static CSPs, each resulting from a restriction or a relaxation of some constraints in the previous CSP constraint set. This paper focuses on constraint restriction (constraint addition) and the goal is to obtain the most similar solution to the previous one that satisfies the old and new constraints. In this regard, we propose a new method based on the Particle Swarm Optimization algorithm to solve these DCSPs with minimal perturbation. To evaluate the efficiency of the proposed method, we conducted extensive experiments on randomly generated DCSP instances generated by the model RB. The results achieved clearly demonstrate the efficiency of the proposed algorithm over other known exact and approximation techniques used in the literature for solving these problems.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".