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Multiobjective Optimization

Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously. Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Minimizing cost while maximizing comfort while buying a car, and maximizing performance whilst minimizing fuel consumption and emission of pollutants of a vehicle are examples of multi-objective optimization problems involving two and three objectives, respectively. In practical problems, there can be more than three objectives.

Papers

Showing 110 of 155 papers

TitleStatusHype
Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via TensorizationCode7
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization ApproachCode7
GPU-accelerated Evolutionary Multiobjective Optimization Using Tensorized RVEACode3
SustainDC: Benchmarking for Sustainable Data Center ControlCode2
LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorchCode2
Large Language Model for Multi-objective Evolutionary OptimizationCode1
Learning the Pareto Front with HypernetworksCode1
DOCKSTRING: easy molecular docking yields better benchmarks for ligand designCode1
Efficient Continuous Pareto Exploration in Multi-Task LearningCode1
A fast balance optimization approach for charging enhancement of lithium-ion battery packs through deep reinforcement learningCode1
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