Swarm heuristic for identifying preferred solutions in surrogate-based multi-objective engineering design

Exploring the entire Pareto frontier of high-fidelity multidisciplinary problems can be prohibitive due to the excessive number of expensive evaluations required. The use of surrogate models offers promise toward managing such problems, which are restricted by a computational budget. In this paper, the kriging-assisted user-preference multi-objective particle swarm heuristic is presented, in which less accurate but inexpensive surrogate models are used cooperatively with the precise but expensive objective functions to alleviate the computational burden. A userpreference module is integrated into the optimization framework, which guides the swarm toward preferred regions of the Pareto frontier, thereby focusing all computing effort on identifying only solutions of interest to the designer. While providing a logical criterion to prescreen candidates for precise evaluation, the additional guidance provided by user-preferences guarantees an accelerated convergence rate. To depict the proficiency of the proposed framework, a suite of test problems, including the multidisciplinary cross-sectional design of a semimonocoque fuselage enclosing a pressurized cabin and payload bay, is presented.Aparametric model is described that is capable of generating a broad range of double-lobe fuselage designs. The superiority of the kriging-assisted user-preference multi-objective particle swarm optimization algorithm over more traditional search methods to efficiently manage high-fidelity discontinuous design problems is highlighted.