S-EPSO: A Socio-Emotional Particle Swarm Optimization Algorithm for Multimodal Search in Low-Dimensional Engineering Applications
Bibliographic record
Abstract
This paper examines strategies aimed at improving search procedures in multimodal, low-dimensional domains. Here, low-dimensional domains refers to a maximum of five dimensions. The present analysis assembles strategies to form an algorithm named S-EPSO, which, at its core, locates and maintains multiple optima without relying on external niching parameters, instead adapting this functionality internally. The first proposed strategy assigns socio-emotional personalities to the particles forming the swarm. The analysis also introduces a technique to help them visit secluded zones. It allocates the particles of the initial distribution to subdomains based on biased decisions. The biases reflect the subdomain’s potential to contain optima. This potential is established from a balanced combination of the jaggedness and the mean-average interval descriptors developed in the study. The study compares the performance of S-EPSO to that of state-of-the-art algorithms over seventeen functions of the CEC benchmark, and S-EPSO is revealed to be highly competitive. It outperformed the reference algorithms 14 times, whereas the best of the latter outperformed the other two 10 times out of 30 relevant evaluations. S-EPSO performed best with the most challenging 5D functions of the benchmark. These results clearly illustrate the potential of S-EPSO when it comes to dealing with practical engineering optimization problems limited to five dimensions.
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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 | 0.000 |
| 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 | 0.001 |
| 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".