Multi-objective optimization through differential evolution for restaurant design

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 se...

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Vydáno v:2016 IEEE Congress on Evolutionary Computation (CEC) s. 2288 - 2295
Hlavní autoři: Cubukcuoglu, Cemre, Chatzikonstantinou, Ioannis, Ekici, Berk, Sariyildiz, Sevil, Tasgetiren, M. Fatih
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2016
Témata:
Algorithm design and analysis
computational design
Evolutionary computation
Investment
multi-objective optimization
Optimization
performance-based design
restaurant optimization
Sociology
Statistics
Visualization
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Shrnutí: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.
DOI:10.1109/CEC.2016.7744071