It is known that the elderly usually spend the last years of their lives indoors, with little contact with others and the outside environment. Indoor environmental quality (IEQ) conditions related to lighting, air quality, thermal comfort, and acoustics directly affect their quality of life. In this study, the main focus is on the design of institutional care rooms for elderly people to create an indoor comfort. However, considering all four factors of IEQ in one model is a challenging task. A multi-objective problem is formulated based on a weighted sum of IEQ components in a parametric modelling environment using computational design methods. Several simulation tools are utilised, and a Self-Adaptive Ensemble Differential Evolution Algorithm is proposed to tackle this complex problem. The results show that optimal ranges for each IEQ component are achieved, with average values reaching 72% of the ideal benchmarks after the algorithm is converged. Results reveal strong correlations between IEQ components. This significant improvement in indoor environmental quality (IEQ) demonstrates the efficacy of the optimisation algorithm used. This study emphasises the flexibility and relevance of these findings for wider implementation in similar settings.@en

Hospitals are known as functionally complex buildings in various ways, namely due to their non-trivial spatial connectivity requirements. A spatial configuration has an impact on human behavior, human movement patterns and should match with the operational logic of the buildings. In hospitals, there are several typical problems that can be attributed to the configuration of the building, namely the inefficient circulation of medical staff, difficult way-finding for visitors, lengthy and complex procedures for patients, long walking times, privacy, hygiene issues and so on. This Ph.D. research aims to develop a computational design methodology for configurational layout optimization of hospital buildings concerning physical matters & human factors, which are directly attributable to the layout/configuration of the hospital. In the optimization models, the considered performance indicators are related with patients (e.g. ease of way-finding), staff (e.g. average walking-time), and operations (e.g. fitness for workflows). Two case studies are studied here as (1) reconfiguration of existing hospitals; and (2) designing the new hospitals by focussing on “layout planning” and “corridor design”. The developed models are programmed in the form of design tool-kits for supporting conceptual design phases. Effectively, this project presents an interdisciplinary methodological framework that can tackle hospital layout design problems by integrating Computational Design workflows, Graph Theory techniques, Operations Research, and Computational Intelligence into the field of Architectural Space Planning. @en

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

Hospital facilities are known as functionally complex buildings. There are usually configurational problems that lead to inefficient transportation processes for patients, medical staff, and/or logistics of materials. The Quadratic Assignment Problem (QAP) is a well-known problem in the field of Operations Research from the category of the facility's location/allocation problems. However, it has rarely been utilized in architectural design practice. This paper presents a formulation of such logistics issues as a QAP for space planning processes aimed at renovation of existing hospitals, a heuristic QAP solver developed in a CAD environment, and its implementation as a computational design tool designed to be used by architects. The tool is implemented in C# for Grasshopper (GH), a plugin of Rhinoceros CAD software. This tool minimizes the internal transportation processes between interrelated facilities where each facility is assigned to a location in an existing building. In our model, the problem of assignment is relaxed in that a single facility may be allowed to be allocated within multiple voxel locations, thus alleviating the complexity of the unequal area assignment problem. The QAP formulation takes into account both the flows between facilities and distances between locations. The distance matrix is obtained from the spatial network of the building by using graph traversal techniques. The developed tool also calculates spatial geodesic distances (walkable, easiest, and/or shortest paths for pedestrians) inside the building. The QAP is solved by a heuristic optimization algorithm, called Iterated Local Search. Using one exemplary real test case, we demonstrate the potential of this method in the context of hospital layout design/re-design tasks in 3D. Finally, we discuss the results and possible further developments concerning a generic computational space planning framework.@en

This paper introduces a Discrete-Event Simulation (DES) tool developed as a parametric CAD program for validating a program of requirements (PoR) for hospital space planning. The DES model simulates the procedures of processing of patients treated by doctors, calculating patient throughput and patient waiting times, based on the number of doctors, patient arrivals, and treatment times. In addition, the tool is capable of defining space requirements by taking hospital design standards into account. Using this tool, what-if scenarios and assumptions on the PoR about space planning can be tested and/or validated. The tool is ultimately meant for reducing patient waiting times and/or increasing patient throughput by checking the match of the layout of a hospital with respect to its procedural operations. This tool is envisaged to grow into a toolkit providing a methodological framework for bringing Operations Research into Architectural Space Planning. The tool is implemented in Python for Grasshopper (GH), a plugin of Rhinoceros CAD software using the SimPy library.@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.

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Architectural design is a process that considers many objectives to satisfy. In general, these objectives are conflicting with each other. On the other hand, many design parameters are associated with these conflicting objectives, too. Therefore, architectural design is described as a complex task. To handle the complexity, computational optimization methods can be employed to investigate architectural design process in detail. This paper focuses on investigating Pareto-front solutions for theatre hall design using multi-objective evolutionary algorithms. To formulate the theatre hall acoustic design problem, we consider three objectives. Two objectives are minimization of both reverberation time, and total initial cost whereas the third objective is the maximization of seating capacity. In addition, several designs and acoustical performance constraints are defined. To tackle this problem, a multi-objective self-adaptive differential evolution algorithm (JDEMO) is proposed and compared with a well-known non-dominated sorting genetic algorithm-II (NSGA-II) from the literature. Computational results show that the proposed JDEMO algorithm achieves competitive results when compared to the NSGA-II.@en

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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Floating neighborhoods are innovative and promising urban areas for challenges in the development of cities and settlements. However, this design task requires a lot of considerations and technical challenges. Computational tools and methods can be beneficial to tackle the complexity of floating neighborhood design. This paper considers the design of a self-sufficient floating neighborhood by using computational intelligence techniques. In this respect, we consider a design problem for locating each neighborhood function in each cluster with a certain density within a floating neighborhood. In order to develop a self-sufficient floating neighborhood, we propose multi-objective evolutionary algorithms, namely, a self-adaptive real-coded genetic algorithm (CGA) as well as a self-adaptive real-coded genetic algorithm (CGA_DE) employing mutation operator of differential evolution algorithm. The only difference between CGA and CGA_DE is the fact that CGA uses random immigration of certain individuals into the population as a mutation operator whereas in the mutation phase of CGA_DE algorithm, the traditional mutation operator DE/rand/1/bin of DE algorithms. The arrangement of individual functions to develop each neighborhood function is further elaborated and formed by using Voronoi diagram algorithm. An application to design a self-sufficient floating neighborhood in Urla district, which is on the west coast of Turkey, İzmir, is presented.@en

This paper discusses a set of façade design alternatives for form-finding problem focusing on conceptual phase. In this respect, the aim of the research is to propose a multi-objective optimization approach for a façade design of public pool building. We present a set of solution belonging to Self-adaptive Multi-objective Ensemble Differential Evolution (JE_DEMO) and Self-adaptive Multi-Objective Differential Evolution (JDEMO) algorithm. We focus on maximization of daylight performance and minimization of structural displacement. Based on results, two algorithms presented competitive results. Contributions are presented based on objectives functions as new trade-offs and proposed JE_DEMO algorithm for design problems.@en

In this paper, the design of a specific case study of a foyer space is concerned in healthcare facility. The design task of a healthcare facility in architectural perspective is one of the most challenging tasks in the architectural design field since it involves different spaces that have unique requirements. Specifically, a foyer space has been considered as a gathering area that answers people’s needs and expectations. The study shows an application of computational intelligence for a skylight design in foyer space. For this reason, objective functions are considered to minimize skylight cost and to maximize the daylight performance of the interior space. Multi-Objective Self-Adaptive Ensemble Differential Evolution Algorithm and Non-Dominated Sorting Genetic Algorithm-II are proposed to tackle this complex problem. According to results, jE_DEMO algorithm presents satisfactory solutions as well as NSGA-II.@en

This paper presents an implementation of multi-objective optimization for a rectangular façade design proposal in a healthcare building’s common space. Objectives are to maximize daylight performance and to minimize façade construction cost. The aim of this study is to enhance indoor comfort of an existing healthcare building by concerning cost-effective façade design alternatives subject to several constraints. To handle the problem, we formulate a multi-objective real-parameter constraint problem. In order to solve this, Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Self-Adaptive Ensemble Differential Evolution (jE_DEMO) algorithms are used. Finally, both algorithms are capable to discover desirable set of design alternatives.@en

The aim of the research is to find a feasible set of theatre hall design alternatives for two objectives, which are the total cost and the reverberation time, subject to several constraints. We formulate the problem as a multi-objective realparameter constrained optimization problem. To handle this problem, we investigated two different optimization algorithms, namely, a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and a multi-objective Harmony Search algorithm (MOHS) in order to gather Pareto front approximation with a set of non-dominated solutions. We demonstrate that the MOHS yields slightly better results than the NSGA-II algorithm.

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This paper presents the results obtained by NSGA-II and jDEMO on a restaurant design optimization in the conceptual phase. A multi-objective problem is formulated by considering the minimization of investment and the maximization of customer count and maximization of visual perception, subject to several constraints. The main problem requires the configuration of restaurant spaces with different seating groups, decisions regarding the customer capacity, fraction and position of the windows. The contributions of the paper can be summarized as follows. We show that most architectural design problems are basically real-parameter multi-objective constrained optimization problems. So, any type of evolutionary and swarm optimization methods can be used in this field. A multi-objective self-adaptive differential evolution algorithm (jDEMO), inspired from the DEMO algorithm from the literature with some modifications, is developed and compared to the well-known fast and non-dominated sorting genetic algorithm so called NSGA-II in order to solve this complex problem and identify alternative design solutions to decision makers. Through the experimental results, we show that the proposed algorithm is competitive with the NSGA-II algorithm.

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This paper is concerned with the application of computational intelligence techniques to the conceptual design and development of a large-scale floating settlement. The settlement in question is a design for the area of Urla, which is a rural touristic region located on the west coast of Turkey, near the metropolis of Izmir. The problem at hand includes both engineering and architectural aspects that need to be addressed in a comprehensive manner. We thus adapt the view as a multi-objective constrained real-parameter optimization problem. Specifically, we consider three objectives, which are conflicting. The first one aims at maximizing accessibility of urban functions such as housing and public spaces, as well as special functions, such as a marina for yachts and a yacht club. The second one aims at ensuring the wind protection of the general areas of the settlement, by adequately placing them in between neighboring land masses. The third one aims at maximizing visibility of the settlement from external observation points, so as to maximize the exposure of the settlement. To address this complex multi-objective optimization problem and identify lucrative alternative design solutions, a multi-objective harmony search algorithm (MOHS) is developed and applied in this paper. When compared to the Differential Evolution algorithm developed for the problem in the literature, we demonstrate that MOHS achieves competitive or slightly better performance in terms of hyper volume calculation, and gives promising results when the Pareto front approximation is examined.@en

This paper is concerned with the application of computational intelligence techniques to the conceptual design and development of a large-scale floating settlement. The settlement in question is a design for the area of Urla, which is a rural touristic region located on the west coast of Turkey, near the metropolis of Izmir. The problem at hand includes both engineering and architectural aspects that need to be addressed in a comprehensive manner. We thus adapt the view as a multi-objective constrained real-parameter optimization problem. Specifically, we consider three objectives, which are conflicting. The first one aims at maximizing accessibility of urban functions such as housing and public spaces, as well as special functions, such as a marina for yachts and a yacht club. The second one aims at ensuring the wind protection of the general areas of the settlement, by adequately placing them in between neighboring land masses. The third one aims at maximizing visibility of the settlement from external observation points, so as to maximize the exposure of the settlement. To address this complex multi-objective optimization problem and identify lucrative alternative design solutions, a multi-objective harmony search algorithm (MOHS) is developed and applied in this paper. When compared to the Differential Evolution algorithm developed for the problem in the literature, we demonstrate that MOHS achieves competitive or slightly better performance in terms of hyper volume calculation, and gives promising results when the Pareto front approximation is examined.@en