Multi-objective optimization through differential evolution for restaurant design

Conference paper (2016)

Authors

C. Çubukçuoglu Yasar University

I. Chatzikonstantinou Design Informatics - Architecture and the Built Environment , Yasar University

B. Ekici Yasar University

I.S. Sariyildiz Yasar University, Design Informatics - Architecture and the Built Environment

Mehmet Fatih Tasgetiren Yasar University

Research Group

Design Informatics (Architecture and the Built Environment) (TU Delft)

Multi-objective optimization Performance-based design Evolutionary computation Computational design Restaurant optimization

More Info

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To reference this document use:

http://resolver.tudelft.nl/uuid:5ea86a0e-8eca-4562-957e-7ccc66be6fd5

Published Date

2016

Language

English

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Faculty

Architecture and the Built Environment

Department

Architectural Engineering +Technology

Research Group

Design Informatics

Abstract

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.