Multiobjective Optimization

17 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.

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.

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.

Max-value Entropy Search for Multi-Objective Bayesian Optimization

belakaria/MESMO NeurIPS 2019

We consider the problem of multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto-set of solutions by minimizing the number of function evaluations.

Deep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural Architectures

sebastianrisi/dip 29 Dec 2019

Deep reinforcement learning approaches have shown impressive results in a variety of different domains, however, more complex heterogeneous architectures such as world models require the different neural components to be trained separately instead of end-to-end.

Pareto Multi-Task Learning

Xi-L/ParetoMTL NeurIPS 2019

Recently, a novel method is proposed to find one single Pareto optimal solution with good trade-off among different tasks by casting multi-task learning as multiobjective optimization.

How to Evaluate Solutions in Pareto-based Search-Based Software Engineering? A Critical Review and Methodological Guidance

taochen/sbse-qi 20 Feb 2020

We then conduct an in-depth analysis of quality evaluation indicators/methods and general situations in SBSE, which, together with the identified issues, enables us to codify a methodological guidance for selecting and using evaluation methods in different SBSE scenarios.

Efficient Continuous Pareto Exploration in Multi-Task Learning

mit-gfx/ContinuousParetoMTL 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.