A Hybrid Giza Pyramids Construction–Crow Search Algorithm–Particle Swarm Optimization (HGPC-CSA-PSO) Framework for Simulating Dynamic Collaborative Grouping in Interpreting Education: A Simulation-Based Exploratory Study

This simulation-based exploratory study examines HGPC-CSA-PSO, a hybrid biomimetic optimization framework integrating the Giza Pyramids Construction Algorithm (GPC), Crow Search Algorithm (CSA), and Particle Swarm Optimization (PSO) for dynamic collaborative grouping in interpreting education. The educational scenario and initialization values are modeling assumptions; no classroom intervention, causal teaching experiment, or independently auditable empirical validation is reported. The scalar proficiency index Qi is used as an education-oriented evaluation-layer measure, whereas the checked-in algorithms optimize their original unweighted coordinate-mean fitness. An illustrative educational-model trajectory reaches its stated scalar threshold after 29 simulated interaction updates. Separately, under the standardized repository configuration, 30 paired runs use the implementation-level termination rule that every coordinate of every simulated student must reach 0.99. Under that configuration, HGPC-CSA-PSO converges in 16.367 ± 1.974 updates and is faster than GPC and CSA (Holm-adjusted p < 0.001 for both), but not significantly different from PSO (Holm-adjusted p = 0.194). These two result layers are not interchangeable and provide model-level computational evidence only.