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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 1120 of 155 papers

TitleStatusHype
Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance AssessmentCode1
Learning the Pareto Front with HypernetworksCode1
ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without RetrainingCode1
Large Language Model for Multi-objective Evolutionary OptimizationCode1
Optimizing fairness tradeoffs in machine learning with multiobjective meta-modelsCode1
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
Explainable Bayesian OptimizationCode0
Evolutionary Multiparty Distance MinimizationCode0
A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next stepsCode0
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