Firstly, we propose a spatial planning algorithm inspired by cellar automaton and spatial growth rules for spatial planning support, i.e. generating multiple subspaces and making their layouts. Their features are that there is less restrictions in the shapes, sizes, and positions of the generated subspaces and gap sizes among the subspaces are controllable. We also show the framework of our final spatial planning support system that consists of (1) a spatial layout generator including the mentioned algorithm and rules as main parts and a visualization part generating layout diagrams and (2) an optimization part which main components, i.e. evolutionary multi-objective optimization (EMO) and interactive evolutionary computation, optimize the generated spatial plans. Secondly, we make a concrete architectural room planning support system based on some parts of the said framework and confirm that the EMO makes the generated architectural room plans converge, experimentally. We confirm the performance of the system using two EMO's with four and six objectives, respectively. We also evaluate the effect of introducing a niche technique into the EMO to obtain the variety of architectural room plans. The experiments showed that the convergence of each objective over generations and variety of architectural room plans among individuals of higher scores. This experimental evaluation implies that the combination of our proposed spatial planning algorithms and spatial growth rules is applicable to spatial planning support systems.
|Number of pages||9|
|Journal||Transactions of the Japanese Society for Artificial Intelligence|
|Publication status||Published - 2009|
All Science Journal Classification (ASJC) codes
- Artificial Intelligence