Multi-Criteria Optimisation

Optimisation strategies 29, used to design systems in different disciplines like engineer- ing, architecture and biology are best described as involving a computational search for the highest performing solution, which can be developed in a defined system, usually characterised by constraints [Burry and Burry 2010]. Based on conflicting objectives, in Multi-Criteria Optimisation (MCO) solution spaces are structured by multiple criteria, resulting in a fitness landscape. Solution spaces are usually evaluated by trade-offs that lead to a set of solutions in a pareto front, not to a single best solutions [Coello et al. 2007]. Most architectural problems are addressing a variety of performance criteria and can only be evaluated by MCO. Understanding the complex trade-offs that emerge in regard to the choice between those criteria exceeds the cognitive capacity of humans. Therefore, the capability of the computer to calculate even high-dimensional trade-offs is a valuable asset in decision support.

While MCO30can be divided into three specific approaches to state priorities “a pri- ori definition”, “a posteriori definition” and “interactive definition” [Fonseca and Fleming 1998] the most commonly used in architectural design is the adjustment of a digital model to some criteria a posteriori. This dominance is mainly due to the digital tools available to designers that need a parametric representation of the architectural geometry. As the geometric representation in this process is fixed to a specific topology, later changes in the representation to accommodate for changing requirements lead to complete reworking of the digital model. This process is often not economically feasible in design processes because design costs are a defining factor.

MCO provides additional design information in early design stages to support design- ers in taking critical decisions at the beginning of the design process. Using exploratory search allows for presentation of performance-based solutions early on in the design pro- cess. Supporting designers in making choices based on a variety of alternatives is a core characteristic of DSS. Intelligent decision support assists architectural practitioners in decision-making processes. Qualitative criteria can be addressed by interactive designer input focused on strategic stages during the decision-making process. Longer optimisation processes can be partially automated so that the designer interaction provides strategic

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The notion of optimisation was challenged through the introduction of novelty search by Lehman [Lehman 2007]. This algorithm reveals the potential to develop high performing candidate solutions without the application of criteria during the search process. Still, it does not allow for highly desirable visual feedback on the performance of solutions in the optimisation process, as mentioned by [Brown et al. 2015].

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Multi-criteria processes are also regularly outperformed by single criteria optimisation processes that can be used for simple design tasks, where the data about the performance of the architectural geometry in various aspects is not needed. The convergence is achieved more quickly in this specific case with direct search algorithms.

guidance and evaluation in regard to crucial decisions about what is otherwise a computer- driven process.

An evolutionary design system that uses HITL technology strengthens a designer’s evaluative capacities and reduces the impact of her or his weaknesses is described as an “ideal decision support system” by [Kim 2005]. The constant evolutionary exploration of the design space accumulates knowledge about certain criteria as a result of the fitness evaluation. Such a support system uses the computer’s analytic and computational ca- pacities to assess the performance of architectural geometries, even in a complex trade-off situation between conflicting criteria. Before thinking about the design of such a system, the characteristics of a DSS must have been to be clarified.

The interaction between designer and computational system needs to mutually en- hance the capacities of both actors. One thinks several moves ahead of the other so that a chess-like interaction process emerges which provides the dynamics to engage the designer in the communicative process. [Kim 2005] presents three core capabilities of DSS and four premises as the basis for an implementation of a DSS, presented in Table 2.1. As style is one of the core concepts in architecture describing the value added to design solutions by designers besides pragmatic considerations (Semper [1852, p. 136-138]), the integration of stylistic considerations 31 into digital tools for decision support is worthwhile.

Table 2.1: Capabilities of and premises for a decision support system Capabilities Premises

Propose options Investigative model creation using HCI Anticipate ramifications Improvement of innovative capabilities

Compile relevant data Acquire observation of designer’s creative behaviour - By experimentation with the digital tool

The adaptivity of a DSS to stylistic development based on the choices made during design processes can take place by integrating learning capabilities into the tool that collects data about the decision-making process. In [Mitchell 1989] the term of “stylistic evolution” is mentioned as a commonplace in the architectural discourse at that time and as a crucial module in the development of a DSS. The module that smoothly adjusts the shape style of a rule-based system to the stylistic development during the design process could also be used to adjust an aesthetic system for the quantitative evaluation of the ‘delightfulness’ of designs. As a way to learn more about the possible stylistic expressions in computational design, I analysed the community of practice that surrounds my field of research, as discussed in the next section.

The a priori use of DSS is based on the argument of Boyd Paulson [Paulson 1976],

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Style in the context of this dissertation could refer to three different terms: (a) architectural style, (b) shape style as shared geometric character of a computational representation, or (c) decision making style. Here style refers to architectural style. In the following, the differentiating terms are used.

which reveals the increase in costs and the decrease in potential impact during design progression toward more mature states. Therefore, decision making in early design stages of architectural design is preferred by many designers. Digital tools that describe a large design space can be used for exploratory search of architectural geometries during early design stages. In early design stages, creative processes take place that require the de- signer to use his or her intuition to steer the design process toward the intended design solution. Therefore, digital tools for exploratory search need to accommodate for frequent adaptation of decision-making paths and use different modes of input for architectural design experiments.

While many aspects, qualities and characteristics of design solutions need to be ex- plored by the designer, clearly quantifiable criteria can be calculated by the computer efficiently. Design processes are frequently determined by many different technical and economic criteria to ensure high performance of architectural geometries. As architectural design is focused on evaluation of designs regarding designer priorities, MCO methods need to be defined in relationship to articulation of these priorities [Marler and Arora 2004]. While some design considerations can be addressed using quantitative criteria, others can be formulated using if-then-rules and implemented in expert systems. However, implicit knowledge that is embodied in the mind of the designer can usually not be spontaneously expressed in language or symbols. Nevertheless, this kind of knowledge can be used to exercise choice over a variety of solutions.

Well-defined MCO addresses the demands of the designer while visually presenting the balancing process as a means to guiding the design process into the direction of solutions with a high degree of efficiency [Brown et al. 2015]. Recent research in the area of MCO has focused on the development of faster algorithms32, as showcased in different comparisons, [Cichocka et al. 2015], [Wortmann and Nannicini 2016] after the first implementation of a single-criteria optimisation process in a parametric design environment was conducted by David Rutten [Rutten 2010]. Later efforts moved toward providing visual feedback to the designer using an interactive approach, e.g. Danhaive and Mueller’s research on interactive optimisation of structural systems [Danhaive and Mueller 2015]. As an implementation of the process in a visual programming environment33, the design freedom that is needed to explore design spaces based on emergent representations is not present in their work.

Interactive evolution of architectural geometries based on emergent representation allows designers to address qualitative aspects and to use their intuition for creatively exploring design spaces. While optimisation is commonly used in architecture and engi- neering as a form-finding tool [Moya Castro 2015], [Danhaive and Mueller 2015], these

32_{“It is however only in the last decade that researchers have attempted to utilise the fast convergence} properties of a priori methods to mitigate or alleviate the drawbacks of multi-objective evolutionary meth- ods.”, as Robert Carrese states in his thesis titled ‘On Aerodynamic Design Optimisation’ [Carrese 2012]. This line of enquiry is a vital area of research.

33_{Visual programming environments typically define hierarchical geometric representation, which lack} flexibility. Those environments therefore limit the potential to unfold creative processes.

Figure 2.6: Screenshot Biomorpher interface

optimisation processes usually do not use interactive evaluation by the designer34. Inves- tigations on the use of interactive evolution in design to facilitate creative decision support or include creative facets into semi-automated design processes are needed to unfold the potential of DSS for architectural design.