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
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.
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.
Max-value Entropy Search for Multi-Objective Bayesian Optimization
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
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
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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
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.
