Abstract
Evolutionary multi-objective optimization (EMO) algorithms have been extensively applied to find multiple near Pareto-optimal solutions over the past 15 years or so. However, EMO algorithms for solving bilevel multi-objective optimization problems have not received adequate attention yet. These problems appear in many applications in practice and involve two levels, each comprising of multiple conflicting objectives. These problems require every feasible upper-level solution to satisfy optimality of a lower-level optimization problem, thereby making them difficult to solve. In this paper, we discuss a recently proposed bilevel EMO procedure and show its working principle on a couple of test problems and on a business decision-making problem. This paper should motivate other EMO researchers to engage more into this important optimization task of practical importance.
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Deb, K., Sinha, A. (2009). An Evolutionary Approach for Bilevel Multi-objective Problems. In: Shi, Y., Wang, S., Peng, Y., Li, J., Zeng, Y. (eds) Cutting-Edge Research Topics on Multiple Criteria Decision Making. MCDM 2009. Communications in Computer and Information Science, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02298-2_3
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DOI: https://doi.org/10.1007/978-3-642-02298-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02297-5
Online ISBN: 978-3-642-02298-2
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