Abstract
Engineers and architects are now turning to use computational aids in order to analyze and solve complex design problems. Most of these problems can be handled by techniques that exploit Evolutionary Computation (EC). However existing EC techniques are slow [8] and hard to understand, thus disengaging the user. Swarm Intelligence (SI) relies on social interaction, of which humans have a natural understanding, as opposed to the more abstract concept of evolutionary change. The main aim of this research is to introduce a new solver Silvereye, which implements Particle Swarm Optimization (PSO) in the Grasshopper framework, as the algorithm is hypothesized to be fast and intuitive. The second objective is to test if SI is able to solve complex design problems faster than EC-based solvers. Experimental results on a complex, single-objective high-dimensional benchmark problem of roof geometry optimization provide statistically significant evidence of computational inexpensiveness of the introduced tool.
Keywords
- Architectural Design Optimization (ADO)
- Particle Swarm Optimization (PSO)
- Swarm Intelligence (SI)
- Evolutionary Computation (EC)
- Structural Optimization
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- 1.
E = 3100 [kN/cm²], G = 1291.67 [kN/cm²], gamma = 25 [kN/m³], alphaT = 1.0E−5[1/C°], fy = 1.67 [kN/cm²].
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Acknowledgements
This work is partially supported by the “THELXINOE: Erasmus Euro-Oceanian Smart City Network”. This is an Erasmus Mundus Action-2 Strand-2 (EMA2/S2) project funded by the European Union.
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Cichocka, J.M., Migalska, A., Browne, W.N., Rodriguez, E. (2017). SILVEREYE – The Implementation of Particle Swarm Optimization Algorithm in a Design Optimization Tool. In: Çağdaş, G., Özkar, M., Gül, L., Gürer, E. (eds) Computer-Aided Architectural Design. Future Trajectories. CAADFutures 2017. Communications in Computer and Information Science, vol 724. Springer, Singapore. https://doi.org/10.1007/978-981-10-5197-5_9
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