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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 91–100 of 155 papers

TitleStatusHype
Theoretical Analyses of Multiobjective Evolutionary Algorithms on Multimodal Objectives—0
Kinetics-Informed Neural Networks—0
Robust path-following control design of heavy vehicles based on multiobjective evolutionary optimization—0
Controllable Pareto Multi-Task Learning—0
Inverse Multiobjective Optimization Through Online Learning—0
Learning the Pareto Front with HypernetworksCode1
Wasserstein Distributionally Robust Inverse Multiobjective Optimization—0
Efficient and Sparse Neural Networks by Pruning Weights in a Multiobjective Learning ApproachCode0
Balancing Common Treatment and Epidemic Control in Medical Procurement during COVID-19: Transform-and-Divide Evolutionary Optimization—0
A Generative Machine Learning-Based Approach for Inverse Design of Multilayer Metasurfaces—0
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