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. 2026 Oct 8;26(19):6342. doi: 10.3390/s26196342
Algorithm 1 Specified SBOA-inspired PSO search for an XGBoost detector
Require: Training partition; decoder; N, G, ρ; two training-only CV partitions
Ensure: Selected hyperparameters and one final XGBoost model
  •   1:

    Initialize normalized particles, velocities and personal/global best scores

  •   2:

    Include the fixed XGBoost reference as the first particle

  •   3:

    for t=0 to G−1 do

  •   4:

       if t=0 then

  •   5:

           Use initial particle positions as candidates

  •   6:

       else

  •   7:

           Compute bounded PSO proposals using Equations (4) and (5)

  •   8:

           Replace proposals for the rotating subset using Equation (6)

  •   9:

       end if

  • 10:

       for each particle’s candidate do

  • 11:

           Decode and evaluate the candidate using Equation (3)

  • 12:

           Update personal and global bests if the score improves

  • 13:

           Apply the PSO or greedy hybrid acceptance rule

  • 14:

       end for

  • 15:

    end for

  • 16:

    Confirm the reference and the best three distinct non-reference candidates

  • 17:

    Select by the average search/confirmation score

  • 18:

    Fit the selected configuration on the full training partition

  • 19:

    Predict the untouched test partition once under the fixed threshold rule