A derivative-free approach to mixed integer constrained multiobjective nonsmooth black-box optimization

In this work, we consider multiobjective optimization problems with both bound constraints on the variables and general nonlinear constraints, where objective and constraint function values can only be obtained by querying a black-box. Furthermore, we consider the case where a subset of the variables can only take integer values. We propose a new linesearch-based solution method and show that every limit point of certain subsequences defined by the algorithm is stationary for the problem. For what concerns the continuous variables, we employ a strategy for the estimation of the Pareto frontier recently proposed in the literature which takes advantage of dense sequences of search directions. The subset of variables that must assume discrete values is dealt with using primitive directions. Numerical results obtained with the proposed method on a set of test problems and comparison with other solution methods show the viability and efficiency of the proposed approach.