HOPSO: Harmonic Oscillator–Based Particle Swarm Optimization (Reference Implementation)
Tier validation warnings
- •T3 requirement: environment_hash must be provided and non-empty
Summary
Reference Python implementation of the Harmonic Oscillator–Based Particle Swarm Optimization (HOPSO) algorithm, corresponding to the published PLOS ONE article.
Claims
- —HOPSO achieves competitive or superior performance versus standard PSO and gradient-based methods on tested benchmark functions.
- —The harmonic oscillator update dynamics produce qualitatively different exploration behavior compared to standard PSO.
Assumptions
- —Benchmark optimization functions used are representative of practically relevant optimization landscapes.
- —Hyperparameters reported in the paper are near-optimal for the tested problem classes.
Links
Reproducibility
Deterministic seed: yes
Replication status: independent
Structural Metrics
Rigor Score 7 / 8structural transparency index
Tier T3 compliance 4 / 5(80% of declared tier requirements met)
✓ Claims documented (at least one)
✓ Deterministic seed documented
✓ At least internal replication
✓ Independent replication confirmed
✗ Environment hash documented
Validation context — Zone: Evidence · N = 4
T0 3
T1 0
T2 0
T3 1 ← this artifact
Artifacts with lower tier: 3 · same tier: 1 · higher tier: 0
Median Rigor Score in zone: 0.5
▶▼Computed classification recommendationmismatch
| Dimension | Declared | Recommended | Reasons |
|---|---|---|---|
| Zone | Evidence | Evidence ✓ | • Independent replication indicates empirical results with strong reproducibility guarantees |
| Tier | T3 | T2 | • Deterministic seed present — T1 requirement satisfied • Replication status: independent • Independent replication attained • No environment hash — T3 requires a documented environment hash for full reproducibility |
| AI Level | A0 | A0 ✓ | • No AI model disclosed — assuming no AI used (A0) |
Recommendations are heuristic — based on reproducibility fields and object type.