We present a simple strategy for multiobjective target-driven optimization. Users or decision makers are asked to express their preferences (based on their previous experience) in terms of desired target objective values to drive the optimization towards the most preferred regions of the Pareto front. This can lead to a more efficient exploration of specific regions of the objective space and reduce the computational cost of finding desirable solutions. This strategy combines a-priori with interactive preference--handling approaches. These methods have been recently given more attention in the evolutionary multiobjective optimization community. The proposed algorithm is described in detail and compared with existing methods. Benchmarks on standard mathematical test functions as well as on a realistic structural engineering sizing optimization problem are provided.
