New operators for integer permutation-based particle swarm optimizer
Bibliographic record
Abstract
This paper introduces new operators to particle swarm paradigm (PSO) that could lead to a stunt flocking in solving a class of combinatorial optimization problem. The term "'stunt flocking" is analogous to clustering around an optimal solution in domain of the problem considered. Instead of looking at PSO as using individuality and sociality, we adopt the viewpoint of exploration (selecting among all available options and observing outcomes) and exploiting (consistently choosing the global best option). Accordingly, we define two new operators, namely: exploration and exploiting operators for a permutation-based particle swarm algorithm. For exploiting operator we use an order-based imitation function to simulate imitation of the global best option. However, exploration operator is carried out as a result of two lower level operations: recalling the best solution in the memory of each particle and random shuffling of some elements in the permutation that represents each particle. Traveling sales person (TSP) and quadratic assignment (QA) problems are considered here for testing the algorithm. Matlab subcommands are used to illustrate how these operators can take place in programming code. Results of implementing the proposed technique to the mentioned problems agree to great extent with the known optimal solutions
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
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".