A Multi-Objective Self-Adaptive Differential Evolution Algorithm for Conceptual High-Rise Building Design

Conference paper (2016)

Authors

B. Ekici Design Informatics - Architecture and the Built Environment , Yasar University

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

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

Mehmet Fatih Tasgetiren Yasar University

Quan Ke Pan Huazhong University of Science and Technology

Research Group

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

Multi-objective optimization Performance-based design Evolutionary computation Computational design High-rise optimization

More Info

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http://resolver.tudelft.nl/uuid:1834f6bf-36f8-4d84-bb29-d42e10415572

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 a multi-objective self-adaptive differential evolution algorithm to solve the form-finding problem of high-rise building design in the conceptual phase. The aim of the research is to reach suitable high-rise design alternatives for hard and soft objectives, which are construction cost per square meter, structural displacement, and visual perception of the spaces from the inside out subject to several constraints that are related with both high-rise construction regulations, and profitability of the spaces. We formulate the problem as a multi-objective realparameter constrained optimization problem for three objectives that are inherently conflicting. To tackle this problem, we developed two different optimization algorithms, namely, a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and a Self-Adaptive Differential Evolution Algorithm (jDE) in order to obtain Pareto fronts with diversified non-dominated solutions. The extensive computational results show that the jDE algorithm yields much more desirable Pareto front than the NSGA-II algorithm.