Multiobjective optimization problems are often solved using methods from scalar-valued optimization: One – or a series of – associated scalarizations are formulated and solved using appropriate methods from scalar-valued optimization. Conversely, tools from multiobjective optimization can be applied in combination with appropriate filtering techniques to approach scalar-valued optimization problems. This shows that both topics have a substantial relationship which can be exploited for mutual benefit. In this work we analyze this relationship and suggest to adapt a deterministic multiobjective branch-and-bound algorithm (originally designed for nonconvex multiobjective optimization problems) to solve scalar-valued optimization problems with nonconvex constraints.

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