Abstract
Finding good (or even just feasible) solutions for Mixed-Integer Nonlinear Programming problems independently of the specific problem structure is a very hard but practically useful task, especially when the objective and/or the constraints are nonconvex. We present a general-purpose heuristic based on Variable Neighbourhood Search, Local Bran-ching, Sequential Quadratic Programming and Branch-and-Bound. We test the proposed approach on the MINLPLib, discussing optimality, reliability and speed.
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Keywords
- Sequential Quadratic Programming
- Variable Neighbourhood
- Mixed Integer Nonlinear Program
- Sequential Quadratic Programming Method
- Sequential Quadratic Programming Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgements
We are very grateful to Prof. Tapio Westerlund for carefully checking all the computational results and informing us of some misprints on the MINLPLib website.
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Liberti, L., Nannicini, G., Mladenović, N. (2009). A Good Recipe for Solving MINLPs. In: Maniezzo, V., Stützle, T., Voß, S. (eds) Matheuristics. Annals of Information Systems, vol 10. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1306-7_9
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