Browsing by Author "Ekici, Berk"

Now showing 1 - 16 of 16

Citation - WoS: 11
Citation - Scopus: 12
A multi-objective self-adaptive differential evolution algorithm for conceptual high-rise building design
(Institute of Electrical and Electronics Engineers Inc., 2016) Berk Ekici; Ioannis Chatzikonstantinou; I. Sevil Sariyildiz; M. Fatih Tasgetiren; Quanke Pan; Ekici, Berk; Sariyildiz, Sevil; Chatzikonstantinou, Ioannis; Tasgetiren, M. Fatih; Pan, Quan-Ke
This paper presents a multi-objective self-adaptive differential evolution algorithm to solve the form-finding problem of high-rise building design in the conceptual phase. The aim of the research is to reach suitable high-rise design alternatives for hard and soft objectives which are construction cost per square meter structural displacement and visual perception of the spaces from the inside out subject to several constraints that are related with both high-rise construction regulations and profitability of the spaces. We formulate the problem as a multi-objective realparameter constrained optimization problem for three objectives that are inherently conflicting. To tackle this problem we developed two different optimization algorithms namely a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and a Self-Adaptive Differential Evolution Algorithm (jDE) in order to obtain Pareto fronts with diversified non-dominated solutions. The extensive computational results show that the jDE algorithm yields much more desirable Pareto front than the NSGA-II algorithm. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 8
Citation - Scopus: 8
Addressing the High-Rise Form Finding Problem by Evolutionary Computation
(IEEE, 2015) Berk Ekici; Seckin Kutucu; I. Sevil Sariyildiz; M. Fatih Tasgetiren; Ekici, Berk; Tasgetiren, M. Fatih; Sariyildiz, I. Sevil; Kutucu, Seckin
This paper aims to examine the application of evolutionary algorithms to the form finding problem of high-rise buildings. In the light of mentioned purpose this study concentrates on the conceptual phase of the design process due to the importance of early design decisions. In this respect multi-objective real-parameter constrained optimization is considered as the method of this study in order to solve high-rise design problem. From the point of evolutionary computation we compare two evolutionary algorithms (NSGA-II and DE) focusing on their computational performance and architectural features of the resulting alternatives. Two objective functions are formulated that specifically focus on structural displacement minimization and construction cost per square meter minimization which are clearly conflicting. As a conclusion we discuss in the context of the high-rise design problem the solutions identified by the NSGA-II and DE algorithms.
Citation - Scopus: 16
Design of rectangular façade modules through computational intelligence
(Institute of Electrical and Electronics Engineers Inc., 2017) Selim Karaman; Berk Ekici; Cemre Cubukcuoglu; Basak Kundakci Koyunbaba; Ilker Kahraman; Ekici, Berk; Karaman, Selim; Cubukcuoglu, Cemre; Koyunbaba, Basak Kundakci; Kahraman, Ilker
This paper presents an implementation of multiobjective 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. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 9
Design of Rectangular Facade Modules through Computational Intelligence Case of Common Space in Healthcare Building
(IEEE, 2017) Selim Karaman; Berk Ekici; Cemre Cubukcuoglu; Basak Kundakci Koyunbaba; Ilker Kahraman; Ekici, Berk; Karaman, Selim; Cubukcuoglu, Cemre; Koyunbaba, Basak Kundakci; Kahraman, Ilker
This paper presents an implementation of multi-objective optimization for a rectangular facade design proposal in a healthcare building's common space. Objectives are to maximize daylight performance and to minimize facade construction cost. The aim of this study is to enhance indoor comfort of an existing healthcare building by concerning cost-effective facade 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.
Citation - WoS: 13
Citation - Scopus: 16
Multi-objective diagrid facąde optimization using differential evolution
(Institute of Electrical and Electronics Engineers Inc., 2015) Ioannis Chatzikonstantinou; Berk Ekici; I. Sevil Sariyildiz; Basak Kundakci Koyunbaba; Ekici, Berk; Chatzikonstantinou, Ioannis; Sariyildiz, I. Sevil; Koyunbaba, Basak Kundakci
Facądes constitute one of the fundamental systems of contemporary buildings. They serve multiple purposes such as to ensure proper indoor climate to provide sufficient daylight but also to create a desirable architectural image. Integration of these aspects makes facąde design a complex task that requires significant effort in order to achieve well-performing results. It is thus desirable that systematic approaches to facąde design are developed. In this study we consider facąde design as a multiobjective optimization problem integrating diverse design criteria namely indoor daylight distribution structural performance and cost. We evaluate design performance by making use of simulation. Consequently we use Differential Evolution (DE) to search for best-tradeoff solutions. We compare the performance of two DE variants using the Hypervolume metric and also through qualitative inspection. We report facąde designs that demonstrate interesting and often unexpected features concluding that the proposed approach may lead to a novel more integrated design process. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 7
Citation - Scopus: 9
Multi-Objective Optimization Through Differential Evolution for Restaurant Design
(IEEE, 2016) Cemre Cubukcuoglu; Ioannis Chatzikonstantinou; Berk Ekici; Sevil Sariyildiz; M. Fatih Tasgetiren; Ekici, Berk; Sariyildiz, Sevil; Chatzikonstantinou, Ioannis; Tasgetiren, M. Fatih; Cubukcuoglu, Cemre
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.
Citation - WoS: 9
Citation - Scopus: 10
Multi-Objective skylight optimization for a healthcare facility foyer space
(Institute of Electrical and Electronics Engineers Inc., 2017) Muhittin Yufka; Berk Ekici; Cemre Cubukcuoglu; Ioannis Chatzikonstantinou; I. Sevil Sariyildiz; Ekici, Berk; Chatzikonstantinou, Ioannis; Sariyildiz, I. Sevil; Yufka, Muhittin; Cubukcuoglu, Cemre
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. © 2017 Elsevier B.V. All rights reserved.
Multi-Performance Based Computational Design in Architecture: Integrated High-Rise Buildings
(2014) Ekici, Berk; Kutucu, Seçkin
Architectural design requires art and science knowledge in order to fulfil mankind's needs. Since high-rise buildings were first established, they have been one of the most important building types for human beings. That is why these buildings include many criteria, which belong to different areas of expertise in order to achieve suitable design alternatives. The main goal of this research is to develop a computational design model for high-rise buildings, based on the integration of multi-performance criteria, which is titled as 'Computational High-Rise | C-HR'. Through this development, it is aimed to discover suitable high-rise buildings based on design variables, defined objectives, and constraint violations. In this respect, NSGA-II and DE evolutionary algorithms are implemented in the model. Eight optimization processes have been studied by generating eight hundred suitable high-rise alternatives for two cases based on different constraint violations. According to results of optimization procedures, four comparisons have been done while evaluating the results of different algorithms and their affect on high-rise buildings. In conclusion, benefits of evolutionary computation and integrated design approaches for multi-performance criteria have been discussed. Keywords: Multi-Performance Evaluation, Computational Design, Integrated Design Approach, High-Rise Buildings, Evolutionary Algorithms
Citation - WoS: 38
Citation - Scopus: 52
Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 1: Background methodology setup and machine learning results
(Elsevier Ltd, 2021) Berk Ekici; Tugce Kazanasmaz; Michela Turrin; M. Fatih Tasgetiren; I. Sevil Sariyildiz; Ekici, Berk; Turrin, Michela; Tasgetiren, M. Fatih; Sariyildiz, I. Sevil; Kazanasmaz, Z. Tugce
Designing high-rise buildings is one of the complex tasks of architecture because it involves interdisciplinary performance aspects in the conceptual phase. The necessity for sustainable high-rise buildings has increased owing to the demand for metropolises based on population growth and urbanisation trends. Although artificial intelligence (AI) techniques support swift decision-making when addressing multiple performance aspects related to sustainable buildings previous studies only examined single floors because modelling and optimising the entire building requires extensive computational time. However different floor levels require various design decisions because of the performance variances between the ground and sky levels of high-rises in dense urban districts. This paper presents a multi-zone optimisation (MUZO) methodology to support decision-making for an entire high-rise building considering multiple floor levels and performance aspects. The proposed methodology includes parametric modelling and simulations of high-rise buildings as well as machine learning and optimisation as AI methods. The specific setup focuses on the quad-grid and diagrid shading devices using two daylight metrics of LEED: spatial daylight autonomy and annual sunlight exposure. The parametric model generated samples to develop surrogate models using an artificial neural network. The results of 40 surrogate models indicated that the machine learning part of the MUZO methodology can report very high prediction accuracies for 31 models and high accuracies for six quad-grid and three diagrid models. The findings indicate that the MUZO can be an important part of designing high-rises in metropolises while predicting multiple performance aspects related to sustainable buildings during the conceptual design phase. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 28
Citation - Scopus: 35
Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 2: Optimisation problems algorithms results and method validation
(Elsevier Ltd, 2021) Berk Ekici; Tugce Kazanasmaz; Michela Turrin; M. Fatih Tasgetiren; I. Sevil Sariyildiz; Ekici, Berk; Turrin, Michela; Tasgetiren, M. Fatih; Sariyildiz, I. Sevil; Kazanasmaz, Z. Tugce
High-rise building optimisation is becoming increasingly relevant owing to global population growth and urbanisation trends. Previous studies have demonstrated the potential of high-rise optimisation but have been focused on the use of the parameters of single floors for the entire design, thus the differences related to the impact of the dense surroundings are not taken into consideration. Part 1 of this study presents a multi-zone optimisation (MUZO) methodology and surrogate models (SMs) which provide a swift and accurate prediction for the entire building design, hence the SMs can be used for optimisation processes. Owing to the high number of parameters involved in the design process the optimisation task remains challenging. This paper presents how MUZO can cope with an enormous number of parameters to optimise the entire design of high-rise buildings using three algorithms with an adaptive penalty function. Two design scenarios are considered for quad-grid and diagrid shading devices glazing type and building-shape parameters using the setup and the SMs developed in part 1. The optimisation part of the MUZO methodology reported satisfactory results for spatial daylight autonomy and annual sunlight exposure by meeting the Leadership in Energy and Environmental Design standards in 19 of 20 optimisation problems. To validate the impact of the methodology optimised designs were compared with 8748 and 5832 typical quad-grid and diagrid scenarios respectively using the same design parameters for all floor levels. The findings indicate that the MUZO methodology provides significant improvements in the optimisation of high-rise buildings in dense urban areas. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 7
Optimisation of energy consumption and daylighting using building performance surrogate model
(UNIV MELBOURNE, 2015) Elif Esra Aydin; Onur Dursun; Ioannis Chatzikonstantinou; Berk Ekici; Ekici, Berk; Chatzikonstantinou, Ioannis; Aydin, Elif Esra; Dursun, Onur; RH Crawford; A Stephan
Today the architects are expected to identify solutions that provide best trade-offs among an excessively large number of possible design alternatives. Within this context computational intelligence techniques prove to be valuable decision support tools. In parallel to this agenda the current study aimed to present a novel approach towards identifying non-dominated design solutions that minimize annual building energy consumption and improve indoor daylight conditions. We applied the method to an L plan shaped office design. In this hypothetical building design parameters of footprint area number of levels fenestration shading U-Values of building elements and HVAC system selection were set as variables, whereas total floor area and floor height were kept as constants in order to facilitate further practical relevance. A total of 105 simulations were performed for various values of the parameters. The resulting dataset was used to obtain two approximation models for each of the objective functions. A Multi-Objective Evolutionary Algorithm was subsequently used to obtain the set of non-dominated solutions for the problem. Our results indicated the applicability of the proposed approach for decision-making practices at the conceptual design phase of relevant cases.
Citation - WoS: 33
Citation - Scopus: 42
OPTIMUS: Self-Adaptive Differential Evolution with Ensemble of Mutation Strategies for Grasshopper Algorithmic Modeling
(MDPI, 2019) Cemre Cubukcuoglu; Berk Ekici; Mehmet Fatih Tasgetiren; Sevil Sariyildiz; Ekici, Berk; Sariyildiz, Sevil; Tasgetiren, Mehmet Fatih; Cubukcuoglu, Cemre
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.
Citation - WoS: 99
Citation - Scopus: 115
Performative computational architecture using swarm and evolutionary optimisation: A review
(Elsevier Ltd, 2019) Berk Ekici; Cemre Cubukcuoglu; Michela Turrin; I. Sevil Sariyildiz; Ekici, Berk; Turrin, Michela; Sariyildiz, I. Sevil; Cubukcuoglu, Cemre
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. © 2018 Elsevier B.V. All rights reserved.
Citation - Scopus: 8
Time-cost optimization at the conceptual design stage using differential evolution
(Institute of Electrical and Electronics Engineers Inc., 2015) Onur Dursun; Berk Ekici; I. Sevil Sariyildiz; Ekici, Berk; Sariyildiz, Sevil; Dursun, Onur
Concurrent minimization of construction cost and duration is a challenging task for architects through conceptual design. Using differential evolution (DE) this study aims to obtain optimum design solutions that minimize unit cost of construction and construction duration. Single family housing projects in Germany is sampled with the intent of developing objective functions with regression analysis. The results suggests that DE is able to converge a set of optimum design solutions after adequate number of generations. Resemblance between descriptive statistics of the sampled observations and DE results underpins that regression models can be employed to develop objective functions in the presence of reliable and structured data. In addition results by the formal test of hypothesis based on hypervolume indicator offers DE/rand/1/bin outperforms DE/rand/2/bin irrespective of building standard. Last but not least solutions offered by DE allows us to discuss insights regarding design economics. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 7
Time-Cost Optimization at the Conceptual Design Stage Using Differential Evolution Case of Single Family Housing Projects in Germany
(IEEE, 2015) Onur Dursun; Berk Ekici; Sevil Sariyildiz; Ekici, Berk; Sariyildiz, Sevil; Dursun, Onur
Concurrent minimization of construction cost and duration is a challenging task for architects through conceptual design. Using differential evolution (DE) this study aims to obtain optimum design solutions that minimize unit cost of construction and construction duration. Single family housing projects in Germany is sampled with the intent of developing objective functions with regression analysis. The results suggests that DE is able to converge a set of optimum design solutions after adequate number of generations. Resemblance between descriptive statistics of the sampled observations and DE results underpins that regression models can be employed to develop objective functions in the presence of reliable and structured data. In addition results by the formal test of hypothesis based on hypervolume indicator offers DE/rand/1/bin outperforms DE/rand/2/bin irrespective of building standard. Last but not least solutions offered by DE allows us to discuss insights regarding design economics.
Towards Self‑Sufficient High‑Rises Performance Optimisation using Artificial Intelligence
(TU Delft, 2022) Berk Ekici; Ekici, Berk; B. Ekici
Population growth and urbanisation trends bring many consequences related to the increase in global energy consumption and CO2 emissions and decrease in arable land per person. Alternative design proposals for sustainable living are on the agenda of researchers and professionals to respond to the needs of the 21st century for a sustainable future. Since the early examples in the 19th century the high-rises have been one of the inevitable buildings of metropolises to provide extra floor space in compact cities. Based on the facts of the 21st century high-rise buildings should fulfil more than provide extra floor space in the limited urban plot. This research suggests “self-sufficient high-rise buildings” that can generate and efficiently consume vital resources in addition to dense habitation for sustainable living. Optimisation of highrise buildings has been the focus of researchers because of significant performance enhancement mainly in energy consumption and generation. However optimisation of self-sufficient high-rise buildings requires the integration of multiple performance aspects related to the vital resources of human beings (e.g. energy food and water) and consideration of large numbers of design parameters related to these multiple performance aspects. Hence the complexity of self-sufficient high-rise buildings is more challenging than optimising regular high-rises that have not been addressed in the literature. The purpose of this dissertation is to present a framework for performance optimisation of self-sufficient high-rise buildings using artificial intelligence focusing on the conceptual phase of the design process. Chapter 1 is the introduction to the dissertation. The necessity and the definition of the self-sufficient high-rise buildings are explained after presenting recent proposals of scholars and professionals related to sustainable living alternatives. Additionally the complexity level of self-sufficiency which consists of four categories as scale period parameters and performance is described by indicating the focus in the overall chart. Until now high-rise buildings have been optimised to improve the energy performance that reflects self-sufficiency only in energy consumption. The contribution of this study which focuses on optimising high-rise buildings for multiple resources (e.g. energy food and water) to decrease their environmental impact is described. The research method consists of four main steps: literature review tool development and pilot study computational method development and case study. After presenting the research problem questions aim objectives and output of the dissertation the research method explains the abovementioned steps. Finally the chapter is concluded by discussing the social and scientific relevance of the research. Chapter 2 presents the literature review on optimising form-finding parameters in performative computational architecture that entails form generation performance evaluation and optimisation. A systematic review is conducted based on multiple databases to elaborate the trends for investigating well-performing design alternatives using optimisation algorithms in the architectural design domain. Therefore the review focuses on studies involving form-finding parameters. One hundred studies are systematically reviewed focusing on swarm and evolutionary optimisation algorithms frequently used in architectural design. The chapter concludes by presenting the gaps and needs considered while developing the optimisation tool and computational framework focusing on form-finding parameters performance evaluation and optimisation applications. Chapter 3 presents the development of the optimisation tool called Optimus and the pilot study to test the efficiency of the multi-zone optimisation approach in high-rises. Part A of Chapter 3 presents the Optimus tool which considers a self-adaptive ensemble evolutionary algorithm that can cope with large numbers of design parameters. Tests 1 and 2 are presented to indicate the relevance of the developed tool based on 30-dimensional Congress on Evolutionary Computation 2005 benchmark problems and a 70-dimensional design problem. Part B explains Test 3 to utilise the efficiency of the multi-zone optimisation approach. The main idea of this method is to divide the building into several subdivisions (zones) to be considered different optimisation problems. The pilot high-rise model considers one of the most used façade parameters reported in Chapter 2 (overhang length) and glazing type for two conflicting daylight metrics predicted by the basic version of artificial neural network models and optimised by the initial version of Optimus tool. Chapter 4 presents the multi-zone optimisation (MUZO) methodology that entails the parametric high-rise model machine learning for surrogate models computational optimisation and decision-making. Part A of this chapter presents the entire methodology and two design scenarios indicated as Tests 4 and 5 to demonstrate the relevance of the MUZO. Both scenarios focusing on quad-grid and diagrid façade designs integrate frequently used form-finding parameters for building shape and façade design reported in Chapter 2. Additionally Part A conducts the machine learning results using the parametric high-rise models to cope with the computationally expensive simulation time while assessing the performance of the entire building. Afterwards Part B presents the optimisation problems and results of both design scenarios using the predictive models developed in Part A and the released version of the Optimus tool presented in Chapter 3. Since the study focuses on optimising the entire design of the high-rise scenarios are considered 260 and 220 design parameters respectively for quad-grid and diagrid scenarios. Consequently Part B presents the relevance of the MUZO methodology by comparing the results with the regular high-rise scenarios which use the same design parameters in the entire building. Chapter 5 investigates utilising the MUZO methodology and Optimus tool to optimise the Europoint complex in Rotterdam the Netherlands for self-sufficiency in terms of energy consumption and food production. The sufficiency in food production is demonstrated for lettuce crops grown in vertical farms. Building-integrated photovoltaic panels are used in several building parts regarding sufficiency in energy. The optimisation problem which involves 117 decision variables related to the façade design and the thermal properties of the glazing addresses the self-sufficiency at the building scale in detail. Moreover another optimisation problem reports the potentials at the neighbourhood scale using the same self-sufficiency aspects and design parameters. Among 13 algorithms used to optimise both problems the Optimus tool presented the most favourable self-sufficiency performance. Chapter 6 concludes the dissertation by summarising the contribution of the research addressing the answers to research questions presenting the limitations of the research and highlighting future recommendations. After completing the development of the optimisation tool and conducting preliminary results of the pilot high-rise model the research results are conducted in weeks instead of years during the development of the MUZO methodology and case study. Thanks to artificial intelligence decision-makers can utilise the proposed computational framework for optimising self-sufficient high-rise buildings. In this way consequences of the decisions on performance aspects of self-sufficiency become possible for such a complex design task with high awareness of the alternatives in search space within a reasonable timeframe. © 2024 Elsevier B.V. All rights reserved.