Berk EkiciTugce KazanasmazMichela TurrinM. Fatih TasgetirenI. Sevil SariyildizEkici, BerkTurrin, MichelaTasgetiren, M. FatihSariyildiz, I. SevilKazanasmaz, Z. Tugce2025-10-0620210038092X0038-092X1471-125710.1016/j.solener.2021.05.0822-s2.0-85107961563https://www.scopus.com/inward/record.uri?eid=2-s2.0-85107961563&doi=10.1016%2Fj.solener.2021.05.082&partnerID=40&md5=e90c45ff6dd73bce0d94bf4ff5d0b2abhttps://gcris.yasar.edu.tr/handle/123456789/8933https://doi.org/10.1016/j.solener.2021.05.082High-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.Englishinfo:eu-repo/semantics/openAccessBuilding Simulation, High-rise Building, Machine Learning, Optimization, Performance-based Design, Sustainability, Architectural Design, Floors, Learning Systems, Optimization, Population Statistics, Structural Design, Sustainable Development, Building Simulation, High Rise Building, Machine-learning, Optimisations, Optimization Algorithms, Optimization Method, Optimization Methodology, Optimization Problems, Performance Based Design, Surrogate Model, Tall Buildings, Algorithm, Artificial Ecosystem, Artificial Intelligence, Building, Detection Method, Equipment, Metropolitan Area, Optimization, SustainabilityArchitectural design, Floors, Learning systems, Optimization, Population statistics, Structural design, Sustainable development, Building simulation, High rise building, Machine-learning, Optimisations, Optimization algorithms, Optimization method, Optimization methodology, Optimization problems, Performance based design, Surrogate model, Tall buildings, algorithm, artificial ecosystem, artificial intelligence, building, detection method, equipment, metropolitan area, optimization, sustainabilityPerformance-Based DesignHigh-Rise BuildingMachine LearningBuilding SimulationOptimizationSustainabilityArticle