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.

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Use these libraries to find Multiobjective Optimization models and implementations
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Most implemented papers

COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting

numbbo/coco 29 Mar 2016

We introduce COCO, an open source platform for Comparing Continuous Optimizers in a black-box setting.

Multiobjective Optimization Training of PLDA for Speaker Verification

sanphiee/MOT-sGPLDA-SRE14 25 Aug 2018

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 19 Jan 2021

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

emi-group/evoxbench 8 Aug 2022

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

emi-group/tensorrvea 1 Apr 2024

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

nvidia/physicsnemo 7 Apr 2024

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 26 Mar 2025

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

zahramajd/GrEA IEEE Transactions on Evolutionary Computation 2013

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

ehw-fit/tf-approximate 11 Jun 2019

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

tailintalent/distillation 23 Aug 2019

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.