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

Berk Ekici; Ioannis Chatzikonstantinou; Sevil Sariyildiz; Mehmet Fatih Tasgetiren; Quan Ke Pan

doi:10.1109/CEC.2016.7744069

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

Berk Ekici, Ioannis Chatzikonstantinou, Sevil Sariyildiz, Mehmet Fatih Tasgetiren, Quan Ke Pan

Design Informatics

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

9 Citations (Scopus)

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.

Original language	English
Title of host publication	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Place of Publication	Piscataway
Publisher	IEEE
Pages	2272-2279
Number of pages	8
ISBN (Electronic)	9781509006229
DOIs	https://doi.org/10.1109/CEC.2016.7744069
Publication status	Published - 2016
Event	2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016

Conference

Conference	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Abbreviated title	CEC 2016
Country/Territory	Canada
City	Vancouver
Period	24/07/16 → 29/07/16

Keywords

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

Access to Document

10.1109/CEC.2016.7744069

Cite this

@inproceedings{1834f6bf36f84d84bb29d42e10415572,

title = "A Multi-Objective Self-Adaptive Differential Evolution Algorithm for Conceptual High-Rise Building Design",

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.",

keywords = "Computational design, Evolutionary computation, High-rise optimization, Multi-objective optimization, Performance-based design",

author = "Berk Ekici and Ioannis Chatzikonstantinou and Sevil Sariyildiz and Tasgetiren, {Mehmet Fatih} and {Ke Pan}, Quan",

year = "2016",

doi = "10.1109/CEC.2016.7744069",

language = "English",

pages = "2272--2279",

booktitle = "2016 IEEE Congress on Evolutionary Computation, CEC 2016",

publisher = "IEEE",

address = "United States",

note = "2016 IEEE Congress on Evolutionary Computation, CEC 2016, CEC 2016 ; Conference date: 24-07-2016 Through 29-07-2016",

}

Ekici, B, Chatzikonstantinou, I, Sariyildiz, S, Tasgetiren, MF & Ke Pan, Q 2016, A Multi-Objective Self-Adaptive Differential Evolution Algorithm for Conceptual High-Rise Building Design. in 2016 IEEE Congress on Evolutionary Computation, CEC 2016., 7744069, IEEE, Piscataway, pp. 2272-2279, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, 24/07/16. https://doi.org/10.1109/CEC.2016.7744069

A Multi-Objective Self-Adaptive Differential Evolution Algorithm for Conceptual High-Rise Building Design. / Ekici, Berk; Chatzikonstantinou, Ioannis; Sariyildiz, Sevil et al.
2016 IEEE Congress on Evolutionary Computation, CEC 2016. Piscataway: IEEE, 2016. p. 2272-2279 7744069.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Tasgetiren, Mehmet Fatih

AU - Ke Pan, Quan

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N2 - 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.

AB - 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.

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

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Keywords

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