Comparative evaluation of some interactive reference point-based methods for multi-objective optimisation

DOI10.1057/palgrave.jors.2600786zbMath1054.90070OpenAlexW2012723160WikidataQ109947375 ScholiaQ109947375MaRDI QIDQ3154400

Marko M. Mäkelä, Kaisa M. Miettinen

Publication date: 13 January 2005

Published in: Journal of the Operational Research Society (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1057/palgrave.jors.2600786

zbMATH Keywords

nonlinear programmingmulti-objective optimisationreference direction methodnonsmooth optimisationNIMBUS

Mathematics Subject Classification ID

Applications of mathematical programming (90C90) Multi-objective and goal programming (90C29)

Related Items (15)

Survey of Bundle Methods for Nonsmooth Optimization ⋮ Coupling distinct MOLP interactive approaches with a novel DEA hybrid model ⋮ Visualization-aided multi-criteria decision-making using interpretable self-organizing maps ⋮ Global formulation for interactive multiobjective optimization ⋮ Experiments with classification-based scalarizing functions in interactive multiobjective optimization ⋮ Implementation aspects of interactive multiobjective optimization for modeling environments: the case of GAMS-NIMBUS ⋮ A multi-objective quantity discount and joint optimization model for coordination of a single-buyer multi-vendor supply chain ⋮ A novel algorithm for solving optimal path planning problems based on parametrization method and fuzzy aggregation ⋮ Modified interactive Chebyshev algorithm (MICA) for non-convex multiobjective programming ⋮ A two-slope achievement scalarizing function for interactive multiobjective optimization ⋮ First-order optimality conditions and duality results for multi-objective optimisation problems ⋮ Using box indices in supporting comparison in multiobjective optimization ⋮ On the Use of Preferential Weights in Interactive Reference Point Based Methods ⋮ Synchronous approach in interactive multiobjective optimization ⋮ A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm

Uses Software

• NIMBUS
• NSOLIB

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