A Particle Swarm Optimization Decomposition Strategy for Large Scale Global Optimization
Bibliographic record
Abstract
Countless large-scale global optimization (LSGO) problems occur in an ever-growing number of professions. Cooperative co-evolution (CC) has been shown to assist in discovering encouraging solutions to such complicated issues effectively. CC does this by breaking down a massive problem into distinct smaller sub-problems, which, when solved, are combined to form a solution to the original problem. How a problem is broken down is referred to as decomposition. CCs performance on LSGO problems is highly dependent on the decomposition used. Numerous LSGO decomposition methods have been introduced to address this issue; however, finding a favourable decomposition is challenging, hinting there is still room for improvement and further exploration. This paper presents a new particle swarm optimization decomposition (PSOD) strategy for tackling LSGO problems. PSOD, in addition to its parameters, is explored, showing they provide statistically significant importance in their selection for a few of the CEC,2013 benchmark functions while being arbitrary for others. Further empirical studies compare PSOD's performance to other leading decomposition algorithms, resulting in PSOD performing best on the fully-separable Ackley function, being interchangeable for a few others, and performing competitively with the rest. PSOD attempts to combine Particle Swarm Optimization (PSO) and Cooperative Particle Swarm Optimization (CPSO) to evolve a decomposition while simultaneously optimizing an objective function for improved performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".