Learning and Intelligent Optimization. 4th International Conference LION 4

Abstract

In the design of complex engineering systems involving multiple disciplines it is critical that the interactions between the subsystems of the problem are accounted
for. Only by considering the fully coupled system can an optimal design emerge. Formal multidisciplinary design optimization (MDO) methods [1] fall into two broad categories; 1) monolithic formulations where a single optimizer addresses the whole problem and 2) multilevel methods where the problem is decomposed along disciplinary lines and optimization takes place at both a system and domain level. The single optimizer approach is simple to implement but can scale poorly for larger problems and increasing number of disciplines. It may also prove problematic in an industrial setting to bring all of the domain analysis tools under the control of a single optimizer. Multilevel architectures promote discipline autonomy. The system level is responsible for managing interactions between disciplines. Such an approach allows design teams to work in relative isolation based upon targets set at the system level. If MDO methods are to
be accepted in an industrial context they must support this form of distributed design optimization for both organizational and computational reasons. In this
work a related approach is proposed; that of replacing the formal system level optimizer with an expert system to reason over information from the domains and make decisions about changes to the common design variables vector or bounds. Such an approach sacrifices, possibly elusive, guarantees of convergence for potentially attractive returns in the enterprise.

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Identifiers

ORCID for A.J. Keane: orcid.org/0000-0001-7993-1569

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