Probabilistic Load-Adaptive Tree-Inspired Section Selection (LATSS) for Sizing Large-Scale Truss Structures

This study evaluates Probabilistic Load-Adaptive Tree-Inspired Section Selection (LATSS) for constrained sizing of six large trusses. Finite-element utilization ratios guide probabilistic growth and pruning, supplemented by staged pruning, group mutation, and elitist restarts. Each comparison uses 30 independently initialized runs of 100,000 design evaluations with exactly matched initial populations. The revision adds 1200 runs, comprising corrected compression equation searches, operator ablations, a simple resizing heuristic, restart-equipped JAYA and TLBO baselines, and a 50 mm displacement sensitivity study. Corrected LATSS best volumes are 21.002 and 23.102 m3 for the 582 and 942 bar models, compared with 21.487 and 23.752 m3 under the earlier equation. LATSS achieves lower best and mean objectives than the implemented differential evolution (DE) on four models; DE performs better on the 200 and 2386 bar models. At 50 mm, the 2386 bar best volumes are 430.422 m3 for LATSS and 406.508 m3 for DE. Removing mutation substantially worsens several large model results, whereas restart and staged pruning do not improve every model. All 1200 evaluation ledgers and final vectors were audited; ten selected new designs passed independent SAP2000 response and model-constraint checks. The evidence supports problem-dependent effectiveness under explicit axial model definitions, rather than universal superiority or complete structural code compliance. Archive-selected designs remain outside controlled statistics. A separate two-model initialization study charges the generation of each warm start design to its total evaluation budget.