Multiobjective Optimization
38 papers with code • 0 benchmarks • 3 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
Libraries
Use these libraries to find Multiobjective Optimization models and implementationsDatasets
Database of axial impact simulations of the crash box
Deep Sea Treasure Pareto-Front
RSM-based multi-objective optimization using desirability functions
Most implemented papers
COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting
We introduce COCO, an open source platform for Comparing Continuous Optimizers in a black-box setting.
Multiobjective Optimization Training of PLDA for Speaker Verification
Most current state-of-the-art text-independent speaker verification systems take probabilistic linear discriminant analysis (PLDA) as their backend classifiers.
ES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search Spaces
google-research/google-research
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In this paper, we approach the problem of optimizing blackbox functions over large hybrid search spaces consisting of both combinatorial and continuous parameters.
Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment
From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems; hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them.
GPU-accelerated Evolutionary Multiobjective Optimization Using Tensorized RVEA
Evolutionary multiobjective optimization has witnessed remarkable progress during the past decades.
Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach
The experimental results show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days.
Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via Tensorization
emi-group/evomo
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To bridge the gap, we propose to parallelize EMO algorithms on GPUs via the tensorization methodology.
A Grid-Based Evolutionary Algorithm for Many-Objective Optimization
Balancing convergence and diversity plays a key role in evolutionary multiobjective optimization (EMO).
ALWANN: Automatic Layer-Wise Approximation of Deep Neural Network Accelerators without Retraining
A suitable approximate multiplier is then selected for each computing element from a library of approximate multipliers in such a way that (i) one approximate multiplier serves several layers, and (ii) the overall classification error and energy consumption are minimized.
Pareto-optimal data compression for binary classification tasks
The goal of lossy data compression is to reduce the storage cost of a data set $X$ while retaining as much information as possible about something ($Y$) that you care about.
