A scalable test suite for bi-objective multidisciplinary optimization

Multidisciplinary design optimization (MDO) involves solving problems that feature multiple subsystems or disciplines, which is an important characteristic of many complex real-world problems. Whilst a range of single-objective benchmark problems have been proposed for MDO, there exists only a limited selection of multi-objective benchmarks, with only one of these problems being scalable in the number of disciplines. In this paper, we propose a new multi-objective MDO test suite, based on the popular ZDT bi-objective benchmark problems, which is scalable in the number of disciplines and design variables. Dependencies between disciplines can be defined directly in the problem formulation, enabling a diverse set of multidisciplinary topologies to be constructed that can resemble more realistic MDO problems. The new problems are solved using a multidisciplinary feasible architecture which combines a conventional multi-objective optimizer (NSGA-II) with a Newton-based multidisciplinary analysis solver. Empirical findings show that it is possible to solve the proposed ZDT-MDO problems but that multimodal problem landscapes can pose a significant challenge to the optimizer. The proposed test suite can help stimulate more research into the neglected but important topic of multi-objective multidisciplinary optimization.