# AR0026-modeFrontier

## modeFrontier

An advanced environment to optimize your design with the help of various genetic algorithms and AI's.

### Introduction

Parametric modelling allows representing geometric entities having editable attributes, and relationships by means of associations. Attributes can be expressed by independent values, which act as input to the model. These are design variables; their eventual variations generate different solutions of the model. Associations allow for processing the data across the related geometric entities; thus the different solutions of the model are generated while respecting the consistency of pre-established relations among geometric entities. Once the parametric model is built, a solution space is obtained. The solution space of the parametric model is constituted by the ensemble of all the possible design alternatives generated by varying the values of the independent parameters of the models. Each design alternative is called an instance of the model.

Systematically generating design alternatives in a 3D modeller allows both a quick visualization of different alternatives and the emergence of un-conceived geometric configurations often based on the high number of possible combinations of the variables; both of which favour the revealing of new design directions, and the disclosing of previously un-expressed design aspects. This potential has great utility for the designer to evaluate visual aspects and explore the variations for aesthetic criteria. Focusing on engineering performance criteria, the analysis of available geometric instances based on simulation software and other performance evaluation processes allows exploring and comparing the instances contained in the solution space of the parametric model with respect to a given set of more sharply defined and measurable design criteria.

For each design solution parametrically generated, performance indicators can be measured and analysed by using building performance simulation tools. Connecting the performance simulation tools to the parametric modeller may allow automate the process.

Design alternatives can be assessed based on how well or how badly they perform in respect to certain performance criteria (design requirements). In order to support the use of appropriate performance assessments, this section introduces the concept of performance indicator and of performance assessment mostly via digital simulations. Specific attention is dedicated to means that are suitable for the early phase of design.

So, the parametric model allows generating many design alternative and the simulation tools allow quantifying relevant performance indicators to assess the design alternatives. However, when the solution space is large, it is not possible to analyse one by one each possible design alternative. Either the designer can select a promising sub-set of design alternatives and limits the analysis to these solutions. Or additional computational tools might be needed to search for good design solutions. This latter case is why optimization algorithms are often used.

In mathematics, optimization is the discipline concerned with finding inputs to a function that minimize or maximize its value, which may be subjected to constraints (Pardalos and Resende, 2002). Optimization algorithms refer to the field of computational optimization, which is the process of designing, implementing and testing computational procedures for solving optimization problems (Baños et al, 2011). Nowadays, several different types of optimization algorithms have been developed. They differ for many aspects, among which the procedure based on which they search for well-performing solutions. In architectural design as in any other fields, the variety of design problems does not allow identifying an absolute preference for a specific type of optimization algorithms. The advantages and disadvantages offered by each optimization techniques should be considered when looking for an appropriate support to a specific design problem. Nevertheless, in architectural design one of the most commonly applied type of optimization algorithms are genetic algorithms.