HOPSO: Harmonic Oscillator–Based Particle Swarm Optimization (Reference Implementation)

Zone: Evidence
Tier: T3
AI: A0
Type: code
Date: 2025-01-01

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 base (T3)5Deterministic seed+1Environment hash—Independent replication+1

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

▶Computed classification recommendationmismatch
DimensionDeclaredRecommendedReasons
ZoneEvidenceEvidence ✓

• Independent replication indicates empirical results with strong reproducibility guarantees

TierT3T2

• 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 LevelA0A0 ✓

• No AI model disclosed — assuming no AI used (A0)

Recommendations are heuristic — based on reproducibility fields and object type.