A survey of a posteriori multi-objective multidisciplinary optimisation methods and benchmarks

Abstract

Multidisciplinary design optimisation (MDO) is a field of optimisation where problems are partitioned into a set of subproblems, or disciplines, with interactions between them. Multi-objective (MO)-MDO considers cases where multiple objectives exist at the problem or subproblem level. Of particular interest are MO-MDO architectures and methods for a posteriori decision making—in which the aim of the optimiser is to produce an approximation of the efficient set of Pareto optimal solutions. A variety of a posteriori MO-MDO methods have been proposed, many of which draw on concepts and tools from evolutionary computation and machine learning. However, these approaches have arisen across a fragmented set of literatures, and there is no unified guide available to support practitioners or steer the progressive development of new methods. This survey aims to provide such a unified perspective. The issue of characterising multi-objectivity in MO-MDO is discussed and a typology is introduced to identify the different methods in an accessible and straightforward way. The available benchmark problems for MO-MDO are also surveyed. Key issues and potential paths for future research in MO-MDO are identified and discussed.