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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 101–125 of 155 papers

TitleStatusHype
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
The Cone epsilon-Dominance: An Approach for Evolutionary Multiobjective Optimization—0
Tchebycheff Procedure for Multi-task Text Classification—0
Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multiobjective Optimization Algorithms—0
Regularisation Can Mitigate Poisoning Attacks: A Novel Analysis Based on Multiobjective Bilevel Optimisation—0
An Assignment Problem Formulation for Dominance Move Indicator—0
How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological GuidanceCode0
MODRL/D-AM: Multiobjective Deep Reinforcement Learning Algorithm Using Decomposition and Attention Model for Multiobjective Optimization—0
Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator—0
Dominance Move calculation using a MIP approach for comparison of multi and many-objective optimization solution sets—0
Expert Learning through Generalized Inverse Multiobjective Optimization: Models, Insights and Algorithms—0
Pareto Multi-Task LearningCode0
Deep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural ArchitecturesCode0
Deep Innovation Protection—0
Pareto-optimal data compression for binary classification tasksCode0
Pareto Optimal Demand Response Based on Energy Costs and Load Factor in Smart Grid—0
Scalarizing Functions in Bayesian Multiobjective Optimization—0
Tuning metaheuristics by sequential optimization of regression models—0
Multiobjective Optimization Training of PLDA for Speaker VerificationCode0
Inferring Parameters Through Inverse Multiobjective Optimization—0
Incorporating Deep Visual Features into Multiobjective based Multi-view Search Results Clustering—0
Multiobjective Test Problems with Degenerate Pareto Fronts—0
Multiobjective Optimization Differential Evolution Enhanced with Principle Component Analysis for Constrained Optimization—0
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