Cemre CubukcuogluIoannis ChatzikonstantinouBerk EkiciI. Sevil SariyildizM. Fatih Tasgetiren2025-10-062016978150900622910.1109/CEC.2016.7744071https://www.scopus.com/inward/record.uri?eid=2-s2.0-85008248192&doi=10.1109%2FCEC.2016.7744071&partnerID=40&md5=18a4b520bc7dc14de2bad1bf17bb65f9https://gcris.yasar.edu.tr/handle/123456789/9758This 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. © 2017 Elsevier B.V. All rights reserved.EnglishComputational Design, Evolutionary Computation, Multi-objective Optimization, Performance-based Design, Restaurant Optimization, Calculations, Constrained Optimization, Decision Making, Genetic Algorithms, Multiobjective Optimization, Optimization, Problem Solving, Computational Design, Design Optimization, Differential Evolution, Multi-objective Constrained Optimization, Multi-objective Problem, Non- Dominated Sorting Genetic Algorithms, Performance Based Design, Self-adaptive Differential Evolution Algorithms, Evolutionary AlgorithmsCalculations, Constrained optimization, Decision making, Genetic algorithms, Multiobjective optimization, Optimization, Problem solving, Computational design, Design optimization, Differential Evolution, Multi-objective constrained optimization, Multi-objective problem, Non- dominated sorting genetic algorithms, Performance based design, Self-adaptive differential evolution algorithms, Evolutionary algorithmsConference Object