Abstract
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.
Highlights
Maximum ( f x_max) and average ( f x_avg) function values in Table 1 further justify the better performance of the Optimus in such a way that the maximum and average function values of Optimus are closer to optimal fitness values in nineteen (19) functions than those yielded by other tools
This paper presented a new optimization plug-in, called Optimus for grasshopper algorithmic modelling (GH) in the Rhinoceros computer aided design (CAD) program
A self-adaptive differential evolution algorithm with ensemble of mutation strategies was implemented in Optimus for architectural design optimization problems
Summary
Architectural design requires different performance aspects to be satisfied as design objectives focus on various topics (e.g., social, economic, physiological, health, safety, structural, cultural, sustainability, etc.) [1]. Some of these performance aspects (e.g., energy, and daylight) require non-linear equations, which increase the level of the complexity. The decisions that are taken in the early design phases have a great impact on the following design stages As a result, they influence the overall performance and the appearance of the constructed building. Computational optimization techniques became a necessity in architectural design
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