DT-GSK: Dimension-Tiered Adaptive Configuration Selection and Deterministic Refinement for Gaining-Sharing Knowledge-Based Optimization Algorithm

Published gaining-sharing knowledge (GSK) variants adapt scalar parameters and donor/selection policy to a single operating point. Dimension-Tiered GSK (DT-GSK) selects its configuration by dimension: an adaptive scaffold serves every tier, a deterministic, budget-exact final refinement runs once at D≥50, with the GSK vector-update equations retained; an exploratory interaction-structure memory supplies the refinement’s basis. Against six GSK-family baselines re-executed on five CEC suites under one budget-fair paired protocol, DT-GSK attains the best descriptive family-rank aggregate on CEC2017 (2.48) and CEC2013 (2.80), though Holm-corrected tests separate it from eGSK on either suite only at D=10; it is second behind eGSK at CEC2017 D=30 and on CEC2011 (Holm-significant loss). On AGSK’s strongest suite, the CEC2020 competition in which it was the runner-up, DT-GSK places fourth; the family panel corroborates AGSK’s published strength in this low-dimensional boundary regime, where every dimension-gated DT-GSK subsystem is inactive (D≤20). On CEC2013LSGO the comparison is family-internal: tied-first descriptive rank; paired tests do not separate DT-GSK from AGSK. Suite roles: CEC2017 selection-exposed; CEC2011/CEC2013 corroborative; CEC2020 pre-registered confirmatory; CEC2013LSGO post hoc. Direct isolations find no standalone benefit from that memory; coordinate axes outperform its basis: controlled negative results. All findings are scoped to the GSK-family panel.