Multiobjective Optimization
30 papers with code • 0 benchmarks • 1 datasets
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.
Benchmarks
These leaderboards are used to track progress in Multiobjective Optimization
Deep Sea Treasure Pareto-Front
Most implemented papers
Efficient Continuous Pareto Exploration in Multi-Task Learning
mit-gfx/ContinuousParetoMTL
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ICML 2020
We present a novel, efficient method that generates locally continuous Pareto sets and Pareto fronts, which opens up the possibility of continuous analysis of Pareto optimal solutions in machine learning problems.
Efficient and Sparse Neural Networks by Pruning Weights in a Multiobjective Learning Approach
malena1906/Pruning-Weights-with-Biobjective-Optimization-Keras
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We suggest a multiobjective perspective on the training of neural networks by treating its prediction accuracy and the network complexity as two individual objective functions in a biobjective optimization problem.
Learning the Pareto Front with Hypernetworks
AvivNavon/pareto-hypernetworks
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ICLR 2021
Here, we tackle the problem of learning the entire Pareto front, with the capability of selecting a desired operating point on the front after training.
End-to-end deep meta modelling to calibrate and optimize energy consumption and comfort
Parameters are estimated by comparing the predictions of the metamodel with real data obtained from sensors using the CMA-ES algorithm, a derivative free optimization procedure.
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems
When these problems are extended to multiobjective ones, it becomes difficult for the existing DRL approaches to flexibly and efficiently deal with multiple subproblems determined by weight decomposition of objectives.
QoS-aware Big Service Composition using Distributed Co-Evolutionary Algorithm
Big services are collections of interrelated web services across virtual and physical domains, processing Big Data.
Using Traceless Genetic Programming for Solving Multiobjective Optimization Problems
Traceless Genetic Programming (TGP) is a Genetic Programming (GP) variant that is used in cases where the focus is rather the output of the program than the program itself.
Benchmark Problems for CEC2021 Competition on Evolutionary Transfer Multiobjectve Optimization
Evolutionary transfer multiobjective optimization (ETMO) has been becoming a hot research topic in the field of evolutionary computation, which is based on the fact that knowledge learning and transfer across the related optimization exercises can improve the efficiency of others.
DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
The field of machine learning for drug discovery is witnessing an explosion of novel methods.
Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method
In addition, this work fully considers the heterogeneity of SNs (i. e. differentiated sensing range and deployment cost) and three-dimensional (3-D) deployment scenarios.
