In this article, a new approach to interactive optimization in industrial design is presented in which, for the first time, implicit preference acquisition methods are integrated. Suitable methods for preference acquisition will be selected, adapted and combined with an own PSO-inspired algorithm. The application of implicit preferences as well as the combined application of implicit and explicit preferences in an interactive optimization represents the main novelty of this contribution since this has not yet been carried out according to the current state of knowledge.Two case studies will be used to test this new approach with regard to convergence and acceptance, and a comparison will be made between the three different kind of optimization (implicit, explicit as well as a combination of both) in terms of their results.

The Implicit Association Test (IAT) is a computerized two-choice discrimination task in which stimuli have to be categorized as belonging to target categories or attribute categories by pressing, as quickly and accurately as possible, one of two response keys. The discrimination association model has been recently proposed for the analysis of reaction time and accuracy of an individual respondent to the IAT. The model disentangles the influences of three qualitatively different components on the responses to the IAT: stimuli discrimination, automatic association, and termination criterion. The article presents General Race (GRace), a MATLAB-based application for fitting the discrimination association model to IAT data. GRace has been developed for Windows as a standalone application. It is user-friendly and does not require any programming experience. The use of GRace is illustrated on the data of a Coca Cola-Pepsi Cola IAT, and the results of the analysis are interpreted and discussed.