On the Pareto Set and Front of Multiobjective Spherical Functions with Convex Constraints

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Abstract

We analyze a fundamental class of multiobjective constrained problems where the objectives are spherical functions and the constraints are convex. As an application from the projection theorem on closed convex sets, we prove that the constrained Pareto set corresponds to the orthogonal projection of the unconstrained Pareto set onto the feasible region. We establish this fundamental geometric property and illustrate its implications using visualizations of Pareto sets and fronts under various constraint configurations. Furthermore, we assess the performance of NSGA-II on these problems, examining its ability to approximate the constrained Pareto set across different dimensions. Our findings highlight the importance of theoretically grounded and understood benchmark problems for assessing algorithmic behavior and contribute to a deeper understanding of constrained multiobjective landscapes.

Original languageEnglish
Title of host publicationGECCO 2025 - Proceedings of the 2025 Genetic and Evolutionary Computation Conference
EditorsGabriela Ochoa
PublisherAssociation for Computing Machinery, Inc
Pages527-535
Number of pages9
ISBN (Electronic)9798400714658
DOIs
Publication statusPublished - 13 Jul 2025
Event2025 Genetic and Evolutionary Computation Conference, GECCO 2025 - Malaga, Spain
Duration: 14 Jul 202518 Jul 2025

Publication series

NameGECCO 2025 - Proceedings of the 2025 Genetic and Evolutionary Computation Conference

Conference

Conference2025 Genetic and Evolutionary Computation Conference, GECCO 2025
Country/TerritorySpain
CityMalaga
Period14/07/2518/07/25

Keywords

  • benchmarking
  • multiobjective constrained optimization
  • test functions

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