Ayca KirimtatBasak Kundakci KoyunbabaIoannis ChatzikonstantinouI. Sevil SariyildizPonnuthurai Nagaratnam Suganthan2025-10-062016978150900622910.1109/CEC.2016.7744286https://www.scopus.com/inward/record.uri?eid=2-s2.0-85008258239&doi=10.1109%2FCEC.2016.7744286&partnerID=40&md5=7293f3ff2884855a1b3cec8458a0b226https://gcris.yasar.edu.tr/handle/123456789/9750The reduction of energy consumption is a major challenge around the world. Architectural aspects have a significant place to minimize energy consumption to the maximum level. The use of large glazed facades causes overheating problems in certain climatic regions. Shading elements must be considered at an early stage in the design process to overcome this problem. An application of the method is presented focusing on the horizontal louvers integrated to a building in Izmir Turkey. 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 generated slightly better results when comparing to the NSGA-II algorithm. © 2017 Elsevier B.V. All rights reserved.EnglishEvolutionary Algorithms, Horizontal Louvers, Multi-objective Optimization, Parametric Modeling, Simulation Modeling, Computer Simulation, Constrained Optimization, Decision Making, Energy Utilization, Genetic Algorithms, Multiobjective Optimization, Optimization, Problem Solving, Alternative Designs, Horizontal Louvers, Multi-objective Constrained Optimization, Non- Dominated Sorting Genetic Algorithms, Nsga-ii Algorithm, Parametric Modeling, Self-adaptive Differential Evolution Algorithms, Swarm Optimization, Evolutionary AlgorithmsComputer simulation, Constrained optimization, Decision making, Energy utilization, Genetic algorithms, Multiobjective optimization, Optimization, Problem solving, Alternative designs, Horizontal louvers, Multi-objective constrained optimization, Non- dominated sorting genetic algorithms, NSGA-II algorithm, Parametric modeling, Self-adaptive differential evolution algorithms, Swarm optimization, Evolutionary algorithmsConference Object