Architectural design optimization

Architectural design optimization (ADO) is a subfield of engineering that uses optimization methods to study, aid, and solve architectural design problems, such as optimal floorplan layout design, optimal circulation paths between rooms, and the like.

Performance-driven architectural design emphasizes on integrated and comprehensive optimization of various quantifiable performances of buildings. As the leading profession of a project team, architects play a vital role in guiding and conducting the performance-driven design. Methodology and techniques start emerging both in literature and practice. However, architects often find them difficult to use for various reasons. Therefore, developing an effective technique to conduct performance-driven design and optimization from the perspective of architects is necessary. Architectural design optimization is a concept of performance-driven architectural design. Existing methodology and techniques are reviewed. The focus is on selecting a basic platform suitable for architects, upon which the technique can be developed.

Architectural design optimization use single- and multi-objective optimization, discuss applications from architectural design and related fields, and survey the three main classes of black-box optimization algorithms: metaheuristics, direct search, and model-based methods. Architectural design optimization tools available to architectural designers and discuss criteria for choosing between different optimization algorithms. Architectural design optimization simulation-based problems from structural, building energy, and daylighting design. Based on empirical results, architectural design optimization use of global direct search and model-based methods over metaheuristics such as genetic algorithms, especially when the budget of function evaluations is limited, for example, in the case of time-intensive simulations. When it is more important to understand the trade-off between performance criteria than to find good solutions and the budget of function evaluations is sufficient to approximate the Pareto front accurately, multi-objective, Pareto-based optimization algorithms.

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