Multi-Stakeholder Non-Dominated Sorting Genetic Algorithm-II
Beyond Single Decision Maker Optimization
Tygo Nijsten (TNO, Universiteit van Amsterdam)
Jan Pieter Dorsman (Universiteit van Amsterdam)
Michel Mandjes (Universiteit van Amsterdam, Universiteit Leiden)
Erwin Walraven (TNO)
Maaike Snelder (TNO, TU Delft - Civil Engineering & Geosciences)
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Abstract
Evolutionary multi-objective optimization (EMO) is widely used to solve problems with competing objectives, yet most existing approaches assume a single decision maker and offer limited support for the diverse and often incomplete preferences found in multi-stakeholder settings. We introduce MS-NSGA-II, an extension of NSGA-II that incorporates heterogeneous stakeholder aspirations without requiring negotiation or repeated interaction. Stakeholders specify only the objectives and aspiration levels relevant to them, and a normalized dissatisfaction measure guides selection while preserving the underlying multi-objective formulation and maintaining diversity in the objective space. Experiments on benchmark problems with up to ten objectives and a real-world area design case study show that MS-NSGA-II significantly reduces stakeholder dissatisfaction and uncovers Pareto-superior regions, particularly in higher-dimensional settings.
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