The paper proposes a generative design workflow for three major hospital layout planning steps to satisfy multiplex configurational requirements. The initial step is stacking through clustering functional spaces into floor plans, for which a spectral method is presented. Subsequently, a novel simultaneous process of zoning and routing is proposed as a Mixed-Integer Programming problem-solving task; performed on a quadrilateral mesh whose faces and edges are allocated respectively to the rooms and the corridors. The paper situates the workflow in the context of an Activity-Relations-Chart for a general hospital while demonstrating, explaining, and justifying the generated optimal floor plans. The conversion of the hospital layout problem to a Mixed-Integer Programming problem enables the use of existing Operations Research solvers, allowing for the generation of optimal solutions in a digital design environment. The comprehensive problem formulation for a real-world scenario opens a new avenue for utilization of mathematical programming/optimization in healthcare design.@en

Most of the architectural design problems are basically real-parameter optimization problems. So, any type of evolutionary and swarm algorithms can be used in this field. However, there is a little attention on using optimization methods within the computer aided design (CAD) programs. In this paper, we present Optimus, which is a new optimization tool for grasshopper algorithmic modeling in Rhinoceros CAD software. Optimus implements self-adaptive differential evolution algorithm with ensemble of mutation strategies (jEDE). We made an experiment using standard test problems in the literature and some of the test problems proposed in IEEE CEC 2005. We reported minimum, maximum, average, standard deviations and number of function evaluations of five replications for each function. Experimental results on the benchmark suite showed that Optimus (jEDE) outperforms other optimization tools, namely Galapagos (genetic algorithm), SilverEye (particle swarm optimization), and Opossum (RbfOpt) by finding better results for 19 out of 20 problems. For only one function, Galapagos presented slightly better result than Optimus. Ultimately, we presented an architectural design problem and compared the tools for testing Optimus in the design domain. We reported minimum, maximum, average and number of function evaluations of one replication for each tool. Galapagos and Silvereye presented infeasible results, whereas Optimus and Opossum found feasible solutions. However, Optimus discovered a much better fitness result than Opossum. As a conclusion, we discuss advantages and limitations of Optimus in comparison to other tools. The target audience of this paper is frequent users of parametric design modelling e.g., architects, engineers, designers. The main contribution of this paper is summarized as follows. Optimus showed that near-optimal solutions of architectural design problems can be improved by testing different types of algorithms with respect to no-free lunch theorem. Moreover, Optimus facilitates implementing different type of algorithms due to its modular system.@en

Urbanization and population growth lead to the construction of higher buildings in the 21st century. This causes an increment on energy consumption as the amount of constructed floor areas is rising steadily. Integrating daylight performance in building design supports reducing the energy consumption and satisfying occupants’ comfort. This study presents a methodology to optimise the daylight performance of a high-rise building located in a dense urban district. The purpose is to deal with optimisation problems by dividing the high-rise building into five zones from the ground level to the sky level, to achieve better daylight performance. Therefore, the study covers five optimization problems. Overhang length and glazing type are considered to optimise spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE). A total of 500 samples in each zone are collected to develop surrogate models. A self-adaptive differential evolution algorithm is used to obtain near-optimal results for each zone. The developed surrogate models can estimate the metrics with minimum 98.25% R2 which is calculated from neural network prediction and Diva simulations. In the case study, the proposed methodology improves daylight performance of the high-rise building, decreasing ASE by approx. 27.6% and increasing the sDA values by around 88.2% in the dense urban district.@en

As integrated components of the building envelopes, shading devices are the elements designed for stopping excessive amount of direct and indirect sunlight passing through and for avoiding undesirable admission of light into glazed buildings. Shading devices also reduce the operational cost of active systems, mostly heating and cooling, by providing considerable energy saving without completely blocking the daylight. However, the conventional shading device types in real world applications and even the ones presented in the literature stick to non-amorphous shapes providing limited improvement of the energy performance with negligible rates. Considering the lack of amorphous solutions in the literature, we propose novel design alternatives of energy-efficient shading device with panels in amorphous forms generated by parametric modeling and performance evaluation-based optimization in contrast with the conventionally designed structures. Initially, a performance evaluation-based optimization model was developed by employing evolutionary multi-objective optimization algorithms to overcome the complexity of the design process. Moreover, minimization of TEC (Total Energy Consumption) and maximization of the UDI (Useful Daylight Illuminance) are defined as the main objective functions to be optimized by non-dominated sorting genetic algorithm (NSGA II) and self-adaptive continuous genetic algorithm with differential evolution (JcGA-DE) in the shading model. According to the numerical results of the annual energy consumption, we managed to reach considerable energy saving up to 14%, while keeping the daylight availability above 50%.@en