A Comparative Study of Multi-Guide Particle Swarm Optimization Topologies in Dynamic Multi-Objective Environments
Bibliographic record
Abstract
Multi-objective optimization problems (MOPs) contain two or three objectives which need to be optimized simultaneously. Rather than having a single optimal solution, MOPs have a set of optimal trade-off solutions. Optimization is made more difficult in the case of dynamic MOPs (DMOPs). Multi-guide particle swarm optimization (MGPSO) has been introduced as a method to optimize static MOPs and DMOPs by optimizing each objective with its own sub-swarm. This paper looks at the impact of using different MGPSO swarm topologies to determine which is the best choice when using MGPSO to solve DMOPs. A benchmark set of 29 dynamic benchmark functions were used to evaluate the performance of each topology. Six performance measures were used for comparison. The results indicate that the ring topology is best suited to the dynamic environments tested here in general, especially Type I and Type II DMOPs. The wheel topology was found to perform best in Type III DMOPs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".