TY - GEN
T1 - A Design Ranking Method for Many-Objective Evolutionary Optimization
AU - Wang, Likai
AU - Tung, Do Phuong Bui
AU - Janssen, Patrick
N1 - Funding Information:
This work is part of the research project: “Optimization Algorithm for Rapid Sustainable Planning and Design”, supported by Housing Development Board (HDB), Singapore.
Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - This study presents a design ranking method for evolutionary optimization that is aimed to address design optimization problems with many performance-related evaluation metrics. The application of the method consists of three strategies. First, all evaluation scores are expressed as percentages that indicate the proportion of the design achieving acceptable performance. Second, related evaluation scores are grouped, and for each group, a combined score is calculated using a weighted product approach. Third, design populations are evolved using the Pareto optimization of the combined evaluation scores. The combination of the three steps helps designers to define and organize the design evaluation metrics and can also produce optimization results revealing meaningful information. A case study is presented to demonstrate the efficacy of the proposed design ranking method. The relevance of the proposed method to performance-based evolutionary optimization research is also discussed.
AB - This study presents a design ranking method for evolutionary optimization that is aimed to address design optimization problems with many performance-related evaluation metrics. The application of the method consists of three strategies. First, all evaluation scores are expressed as percentages that indicate the proportion of the design achieving acceptable performance. Second, related evaluation scores are grouped, and for each group, a combined score is calculated using a weighted product approach. Third, design populations are evolved using the Pareto optimization of the combined evaluation scores. The combination of the three steps helps designers to define and organize the design evaluation metrics and can also produce optimization results revealing meaningful information. A case study is presented to demonstrate the efficacy of the proposed design ranking method. The relevance of the proposed method to performance-based evolutionary optimization research is also discussed.
KW - Architectural Design Optimization
KW - Design Evaluation
KW - Many-Objective Evolutionary Optimization
KW - Performance-based Design
UR - http://www.scopus.com/inward/record.url?scp=85169040963&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-37189-9_11
DO - 10.1007/978-3-031-37189-9_11
M3 - Conference Proceeding
AN - SCOPUS:85169040963
SN - 9783031371882
T3 - Communications in Computer and Information Science
SP - 159
EP - 173
BT - Computer-Aided Architectural Design. INTERCONNECTIONS
A2 - Turrin, Michela
A2 - Andriotis, Charalampos
A2 - Rafiee, Azarakhsh
PB - Springer Science and Business Media Deutschland GmbH
T2 - 20th International Conference on Computer-Aided Architectural Design Futures, CAAD Futures 2023
Y2 - 5 July 2023 through 7 July 2023
ER -