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arXiv · 2610.26202

Hybrid metaheuristics for architectural design optimization under small evaluation and wall-clock budgets

Luciano Ambrosini
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Abstract

Architectural and structural design optimization in parametric software affords about a thousand evaluations of an expensive model for problems with tens of variables, a regime for which most metaheuristics were not designed. On the side of artificial intelligence, two hybrid metaheuristics are proposed: a covariance matrix adaptation evolution strategy (CMA-ES) whose sampling is enriched with differential-evolution trial vectors, and a particle swarm with success-history parameter adaptation, differential-evolution injection and local refinement. A budget-aware delay of population-size reduction in adaptive differential evolution is also proposed. Under a hard cap of 1,200 evaluations, all counted, the CMA-ES hybrid ranks first on seven shifted and rotated functions at 10 and 30 variables and beats CMA-ES on six of seven functions at 30 variables. Ablations attribute this gain to the injection and show that a local search pays off only when its result re-enters the population. The delayed schedule improves three adaptive differential-evolution variants in 40 of 51 comparisons, with no loss. On the side of engineering application, in Grasshopper the CMA-ES hybrid and plain CMA-ES are never significantly worse than the optimizers of Galapagos, SilverEye, Opossum and Tunny on travelling-salesman instances and on a built 85-variable free-form shell, where they end 48 % below the best external tool in 15 minutes. On a 22-variable building-performance problem with binary choices, differential evolution leads instead. Shifting and rotating the functions reverses the rank of Harris hawks optimization. Solvers should therefore be selected by problem structure and stopping time, not by a single ranking.

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Luciano Ambrosini. 2026-10-19. Hybrid metaheuristics for architectural design optimization under small evaluation and wall-clock budgets. https://arxiv.org/abs/2610.26202

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