Efficient evolutionary approach to approximate the Pareto-optimal set in multiobjective optimization, UPS-EMOA

Abstract

Solving real-life engineering problems requires often multiobjective, global, and efficient (in terms of objective function evaluations) treatment. In this study, we consider problems of this type by discussing some drawbacks of the current methods and then introduce a new population-based multiobjective optimization algorithm UPS-EMOA which produces a dense (not limited to the population size) approximation of the Pareto-optimal set in a computationally effective manner.

Keywords:

• efficient Pareto-optimal set approximation
• multicriteria optimization
• population-based approaches
• Pareto-optimality
• non-dominance
• EMO

Acknowledgements

We wish to thank Professor Kalyanmoy Deb and especially Mr Karthik Sindhya, for providing assistance with the use of NSGA-II and some test problems. We are also grateful to Dr Yi Cao for his great assistance with the Monte Carlo-based hypervolume estimation measure, as well as to Mr Saku Kukkonen for providing an implementation for the GD metric. Further, we want to thank Mr Sauli Ruuska for interesting discussions and implementation of the WFG toolkit and Mr Vesa Ojalehto for helping in some technical issues. This study was financially supported by the research grant from the Jenny and Antti Wihuri Foundation.