A two-slope achievement scalarizing function for interactive multiobjective optimization
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Publication:1762156
DOI10.1016/j.cor.2011.10.002zbMath1251.90354OpenAlexW1994355607WikidataQ109314685 ScholiaQ109314685MaRDI QIDQ1762156
Publication date: 15 November 2012
Published in: Computers \& Operations Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cor.2011.10.002
Related Items (6)
An exact scalarization method with multiple reference points for bi-objective integer linear optimization problems ⋮ Comparing reference point based interactive multiobjective optimization methods without a human decision maker ⋮ Approximating the Pareto set of multiobjective linear programs via robust optimization ⋮ Modified interactive Chebyshev algorithm (MICA) for non-convex multiobjective programming ⋮ Optimization with Stochastic Preferences Based on a General Class of Scalarization Functions ⋮ On the use of the \(L_{p}\) distance in reference point-based approaches for multiobjective optimization
Uses Software
Cites Work
- Global formulation for interactive multiobjective optimization
- Multiobjective optimization. Interactive and evolutionary approaches
- Theory of multiobjective optimization
- A visual interactive method for solving the multiple criteria problem
- On the completeness and constructiveness of parametric characterizations to vector optimization problems
- Multiple objective decision making - methods and applications. A state- of-the-art survey. In collaboration with Sudhakar R. Paidy and Kwangsun Yoon
- Nonlinear multiobjective optimization
- Hierarchical generation of Pareto optimal solutions in large-scale multiobjective systems
- The ``Light Beam Search approach. -- An overview of methodology and applications
- A comparison of two reference point methods in multiple objective mathematical programming.
- On scalarizing functions in multiobjective optimization
- Experiments with classification-based scalarizing functions in interactive multiobjective optimization
- An additive achievement scalarizing function for multiobjective programming problems
- Synchronous approach in interactive multiobjective optimization
- Comparative evaluation of some interactive reference point-based methods for multi-objective optimisation
- A classification of the weighting schemes in reference point procedures for multiobjective programming
- A mathematical basis for satisficing decision making
- A naïve approach for solving MCDM problems: the GUESS method
- Linear programming with multiple objective functions: Step method (stem)
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