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
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Deep Sea Treasure Pareto-Front
Latest papers
GPU-accelerated Evolutionary Multiobjective Optimization Using Tensorized RVEA
Evolutionary multiobjective optimization has witnessed remarkable progress during the past decades.
Explainable Bayesian Optimization
In industry, Bayesian optimization (BO) is widely applied in the human-AI collaborative parameter tuning of cyber-physical systems.
Inverse Transfer Multiobjective Optimization
In this paper, we introduce a novel concept of inverse transfer in multiobjective optimization.
Large Language Model for Multi-objective Evolutionary Optimization
It is also promising to see the operator only learned from a few instances can have robust generalization performance on unseen problems with quite different patterns and settings.
A multiobjective continuation method to compute the regularization path of deep neural networks
aamakor/continuation-method
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To overcome this limitation, we present an algorithm that allows for the approximation of the entire Pareto front for the above-mentioned objectives in a very efficient manner for high-dimensional DNNs with millions of parameters.
Optimizing fairness tradeoffs in machine learning with multiobjective meta-models
We present a flexible framework for defining the fair machine learning task as a weighted classification problem with multiple cost functions.
A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps
In order to deploy machine learning in a real-world self-driving laboratory where data acquisition is costly and there are multiple competing design criteria, systems need to be able to intelligently sample while balancing performance trade-offs and constraints.
The Hypervolume Indicator Hessian Matrix: Analytical Expression, Computational Time Complexity, and Sparsity
Also, for the general $m$-dimensional case, a compact recursive analytical expression is established, and its algorithmic implementation is discussed.
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.
Evolutionary Multiparty Distance Minimization
In the field of evolutionary multiobjective optimization, the decision maker (DM) concerns conflicting objectives.
