HCQ-SGuA: A Plant-Growth-Inspired Hybrid Saplings Growing Up Algorithm with Quasi-Random Initialization and Chaotic Operator Control for Continuous Optimization

Metaheuristic optimizers often suffer from premature convergence, diversity loss, and stagnation in complex search landscapes. This study proposes HCQ-SGuA, a plant-growth-inspired framework that strengthens the Sowing phase through structured population initialization and regulates the biologically framed Mating, Branching, and Vaccinating mechanisms through chaotic operator control. An architecture-aware development–confirmation–holdout protocol was used to isolate initialization, chaotic control, their factorial interaction, and adaptive map selection on the 29 CEC2017 function classes available in OPFUNU 1.0.4 at D = 10 and D = 30. Faure initialization and Logistic control were the most consistent individual components, and high-budget confirmation selected HCQ-SGuA2[Faure + Logistic]. On the reserved holdout set, the frozen hybrid was competitive with its individual components and several established optimizers, but it did not significantly surpass the principal nonadaptive SGuA2 variants and remained inferior to DE and L-SHADE. The engineering experiments produced feasible and competitive best-run solutions, although mean performance and stability were weaker than those of the leading differential-evolution methods. The principal contribution is therefore a controlled biomimetic hybridization framework, showing when complementary interventions in Sowing and Growing Up cooperate and when added adaptive complexity is not beneficial.