This study presents a systematic review and summary of performative computational architecture using swarm and evolutionary optimisation. The taxonomy for one hundred types of studies is presented herein that includes different sub-categories of performative computational architecture, such as sustainability, cost, functionality, and structure. Specifically, energy, daylight, solar radiation, environmental impact, thermal comfort, life-cycle cost, initial and global costs, energy use cost, space allocation, logistics, structural assessment, and holistic design approaches, are investigated by considering their corresponding performance aspects. The main findings, including optimisation and all the types of parameters, are presented by focussing on different aspects of buildings. In addition, usage of form-finding parameters of all reviewed studies and the distributions for each performance objectives are also presented. Moreover, usage of swarm and evolutionary optimisation algorithms in reviewed studies is summarised. Trends in publications, published years, problem scales, and building functions, are examined. Finally, future prospects are highlighted by focussing on different aspects of performative computational architecture in accordance to the evidence collected based on the review process.

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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.