Differential evolutionary cuckoo-search-integrated tabu-adaptive pattern search (DECS-TAPS): a novel multihybrid variant of swarm intelligence and evolutionary algorithm in architectural design optimization and automation

One of the critical limitations in architectural design optimization (ADO) is slow convergence due to high-dimensional and multiscale variables. For the rapid and optimal digital prototyping of architectural forms, this paper proposes a novel metaheuristic optimization technique that hybridizes standard low-level algorithms: the differential evolutionary cuckoo-search-integrated tabu-adaptive pattern search (DECS-TAPS). We compared DECS-TAPS to 10 major standard algorithms and 31 hybrids through 14 benchmark tests and investigated multi-objective ADO problems to prove the computational effectiveness of multiple algorithm hybridization. Our findings show that DECS-TAPS is vastly efficient and superior to the covariance matrix adaptation evolution strategy algorithm in multifunnel and weak structural functions. The global sensitivity analysis demonstrated that integrating multiple algorithms is likely conducive to lowering parameter dependence and increasing robustness. For the practical application of DECS-TAPS in building simulation and design automation, Zebroid-a Rhino Grasshopper (GH) add-on-was developed using IronPython and the GH visual scripting language.

Automated summary

This paper proposes a novel metaheuristic optimization technique DECS-TAPS for rapid and optimal digital prototyping of architectural forms. The findings show that DECS-TAPS outperforms other algorithms in multi-objective optimization and computational effectiveness, while also reducing parameter dependence and increasing robustness.