Robust optimization of multi-scenario many-objective problems with auto-tuned utility function

ABSTRACT

The main focus in optimization studies, including multi-objective and multi-scenario optimization, is placed on finding the global optimum or global Pareto-optimal solutions, representing the best possible objective values. Manual preferences and selection are required to obtain a single implementable solution from the obtainable Pareto set. For achieving such a single optimum, the multi-scenario property is applied to adjust the required preference weights automatically; as a result, robust performance for various scenarios is provided. The robust quality definition is believed to decrease the performance uncertainty owing to any possible scenario in real-world settings. These challenges have motivated the creation of a method that searches a robust solution using evolutionary algorithms and well-applied IT tools. This selection process applies two robustness aspects simultaneously: the necessary weights for the aggregation of multi-scenario and multi-objective dimensions are adjusted to obtain similar performances for scenarios as much as possible; the robustness against system parameters is evaluated by the calculation of the robustness indices. The method can be used universally where the robust optimum is required for a model having multi-scenario and multi-objective properties. The proposed method has been tested on test functions, and in conclusion it has been applied to two engineering problems.

KEYWORDS:

• Robust optimum
• multi-scenario
• multi-objective optimization
• sensitivity analysis
• simulation model

Acknowledgments

This work/publication is supported by the EFOP-3.6.1-16-2016-00003 project. The project is co-financed by the European Social Fund.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://github.com/gkiar/NeuroModelling/blob/master/single_neuron/MorrisLecar/NumJacob.m

2 http://appl-dsp.com/wp-content/uploads/2014/03/mlib_MSMO_optimization.zip

Additional information

Funding

This work/publication is supported by the EFOP-3.6.1-16-2016-00003 project. The project is co-financed by the European Social Fund.