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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

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Showing 11–20 of 155 papers

TitleStatusHype
Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance AssessmentCode1
Optimizing fairness tradeoffs in machine learning with multiobjective meta-modelsCode1
End-to-end deep meta modelling to calibrate and optimize energy consumption and comfortCode1
ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without RetrainingCode1
DOCKSTRING: easy molecular docking yields better benchmarks for ligand designCode1
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization ProblemsCode1
A fast balance optimization approach for charging enhancement of lithium-ion battery packs through deep reinforcement learningCode1
An Empirical Study of Diversity of Word Alignment and its Symmetrization Techniques for System Combination—0
An Efficient Approach for Solving Expensive Constrained Multiobjective Optimization Problems—0
Multi Agent Reinforcement Learning Trajectory Design and Two-Stage Resource Management in CoMP UAV VLC Networks—0
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