A Comparative Study of Multi-Guide Particle Swarm Optimization Topologies in Dynamic Multi-Objective Environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it