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Companies daily need to optimize their products, hence optimization plays a significant role in today's design cycle. Problems related to one or more than one objective, originate in several disciplines; typically using a single optimization technology is not sufficient to deal with reallife problems, particularly when the design concerns complex and expensive products. Therefore, engineers are frequently asked to solve problems with several conflicting objective functions. The multiobjective optimization approach provides a set of nondominant designs (Pareto optimality) where a further improvement for one objective is at the expense of all the others: this allows designers to choose the best solution for each scenario.
Solving realworld multiobjective problems is not simple, engineers must address problems connected to the nonlinearity of the functions, complexity of the physics and the computational cost that snowballs as the number of parameters increases. Moreover, the coupling between disciplines for design a product can be really challenging, involving several complicating factors, such as the limitation on the computational resources, and even a lack of communication between different departments.
This tutorial is a survey on methodologies to approach design optimization process, a set of best practices intended for rapid delivery of highquality products, with a specific focus on the numerical algorithms and postprocessing used for selecting optimal design configurations.
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