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Multidisciplinary Design Optimization (MDO) is often required in aircraft design to address the multidisciplinary feasibility issues due to the disciplines, for example, aerodynamics, propulsion, and structures, are heavily coupled. However, in automobile designs, can we apply different type of MDO decomposition originated from aircraft design, to some MDO problem, for example, a vehicle weight reduction example? Also, to effectively and efficiently accommodate design changes, multi-party collaboration between discipline specialists, and fast decision making, a web-based MDO platform with knowledge-based repository for resource sharing, capability of version control, and enhancing data security, is very much needed. Two types of MDO decomposition: All-at-Once (AAO) and Collaborative Optimization (CO) are formulated for the weight reduction example. A typical six-step MDO process, from building single discipline work flow to comparing optimization results, is illustrated step-by-step.

In recent years, the use of engineering design optimization techniques has grown multifold and formal optimization has become very popular among design engineers. However, the real world problems are turning out to be involved and more challenging. It is not uncommon to encounter problems with a large number of design variables, objectives and constraints. The engineers’ expectation, that an optimization algorithm should be able to handle multi-objective, multi-constrained data is leading them to apply optimization techniques to truly large-scale problems with extremely large number of constraints and objectives. Even as newer and better optimization algorithms are being developed to tackle such problems, more often than not, the optimization algorithms are unable to find a single feasible design that satisfies all constraints.

With the increase in computational capability, there is an increase in classes of engineering optimization problems that are considered solvable. Not all problems benefit from similar types of approaches when searching for an optimal solution. Some have objective functions that can be described as largely unimodal while others have complex behavior with multiple local optima. Further, there are problems that have behavior that is not clearly apparent due to the involvement of CAD/CAE tools and high number of inputs/factors. There has been a push to combine dissimilar optimization approaches in order to tackle such hard-to-solve problems for a variety of reasons. One such combination is the “Hybrid” optimization algorithm developed by ESTECO for their commercial optimization software “modeFRONTIER”. This paper gives the reader some examples and results from problems where the Hybrid algorithm has proved to be a worthy choice.