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Possibilistic framework for multi-objective optimization under uncertainty. (English) Zbl 1444.90106
Talbi, El-Ghazali (ed.) et al., Bioinspired heuristics for optimization. Selected papers of the 6th international conference on metaheuristics and nature inspired computing, Marrakech, Morocco, October 27– 31, 2016. Cham: Springer. Stud. Comput. Intell. 774, 1-26 (2019).
Summary: Optimization under uncertainty is an important line of research having today many successful real applications in different areas. Despite its importance, few works on multi-objective optimization under uncertainty exist today. In our study, we address a combinatorial multi-objective problem under uncertainty using the possibilistic framework. To this end, we firstly propose new Pareto relations for ranking the generated uncertain solutions in both mono-objective and multi-objective cases. Secondly, we suggest an extension of two well-known Pareto-base evolutionary algorithms namely, SPEA2 and NSGAII. Finally, the extended algorithms are applied to solve a multi-objective Vehicle Routing Problem (VRP) with uncertain demands.
For the entire collection see [Zbl 1423.90007].
MSC:
90C29 Multi-objective and goal programming
90C15 Stochastic programming
90C59 Approximation methods and heuristics in mathematical programming
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