Abstract
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.
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Acknowledgement
This work is part of the research project: “Optimization Algorithm for Rapid Sustainable Planning and Design”, supported by Housing Development Board (HDB), Singapore.
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Wang, L., Tung, D.P.B., Janssen, P. (2023). A Design Ranking Method for Many-Objective Evolutionary Optimization. In: Turrin, M., Andriotis, C., Rafiee, A. (eds) Computer-Aided Architectural Design. INTERCONNECTIONS: Co-computing Beyond Boundaries. CAAD Futures 2023. Communications in Computer and Information Science, vol 1819. Springer, Cham. https://doi.org/10.1007/978-3-031-37189-9_11
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