This paper addresses the problem of optimizing mechanical components during the first stage of the design process. While a previous study focused on parameterized designs with fixed configurations-which which led to the development of the PAMUC (Preferences Applied to Multiobjectivity and Constraints) method, to tackle constraints and preferences in evolutionary algorithms (EAs)-, the models to be considered in this work are enriched by the presence of topological variables.
In this context, in order to create optimal but also realistic designs, i.e. fulfilling not only technical requirements but also technological constraints (more naturally expressed in terms of rules), a novel approach is proposed: PAMUC II. It consists in integrating an inference engine within the EA to repair the individuals violating the user-defined rules. PAMUC II is tested on mechanical benchmarks, and provides very satisfactory results in comparison with a weighted sum method with penalization to deal with the constraints.