Papers Multiobjective Optimization
“Multiobjective Optimization” 태그가 달린 논문 155편 · 필터 해제
Two novel feature selection algorithms based on crowding distance
In this paper, two novel algorithms for features selection are proposed. The first one is a filter method while the second is wrapper method. Both the proposed algorithms use the crowding distance used in the multiobject…
Attributefeature selectionMultiobjective OptimizationVocal Bursts Valence PredictionAn Analysis of Phenotypic Diversity in Multi-Solution Optimization
More and more, optimization methods are used to find diverse solution sets. We compare solution diversity in multi-objective optimization, multimodal optimization, and quality diversity in a simple domain. We show that m…
DiversityMultiobjective OptimizationMeta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems
Deep reinforcement learning (DRL) has recently shown its success in tackling complex combinatorial optimization problems. When these problems are extended to multiobjective ones, it becomes difficult for the existing DRL…
Combinatorial OptimizationDeep Reinforcement LearningDiversityMeta-Learning+4Heterogeneous Objectives: State-of-the-Art and Future Research
Multiobjective optimization problems with heterogeneous objectives are defined as those that possess significantly different types of objective function components (not just incommensurable in units or scale). For exampl…
Multiobjective OptimizationAn Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization
Real-world multiobjective optimization problems usually involve conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto optimal front (POF) when the environmen…
Evolutionary AlgorithmsMultiobjective OptimizationEnd-to-end deep meta modelling to calibrate and optimize energy consumption and comfort
In this paper, we propose a new end-to-end methodology to optimize the energy performance as well as comfort and air quality in large buildings without any renovation work. We introduce a metamodel based on recurrent neu…
Multiobjective OptimizationES-ENAS: Efficient Evolutionary Optimization for Large Hybrid Search Spaces
In this paper, we approach the problem of optimizing blackbox functions over large hybrid search spaces consisting of both combinatorial and continuous parameters. We demonstrate that previous evolutionary algorithms whi…
Combinatorial OptimizationContinuous ControlEvolutionary AlgorithmsMultiobjective Optimization+2Manifold Interpolation for Large-Scale Multi-Objective Optimization via Generative Adversarial Networks
Large-scale multiobjective optimization problems (LSMOPs) are characterized as involving hundreds or even thousands of decision variables and multiple conflicting objectives. An excellent algorithm for solving LSMOPs sho…
DiversityEvolutionary AlgorithmsGenerative Adversarial NetworkMultiobjective OptimizationUsing BART to Perform Pareto Optimization and Quantify its Uncertainties
Techniques to reduce the energy burden of an industrial ecosystem often require solving a multiobjective optimization problem. However, collecting experimental data can often be either expensive or time-consuming. In suc…
Gaussian ProcessesMultiobjective OptimizationTheoretical Analyses of Multiobjective Evolutionary Algorithms on Multimodal Objectives
The theoretical understanding of MOEAs is lagging far behind their success in practice. In particular, previous theory work considers mostly easy problems that are composed of unimodal objectives. As a first step towards…
2kEvolutionary AlgorithmsMultiobjective OptimizationOn the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation
We present a novel algorithm that allows us to gain detailed insight into the effects of sparsity in linear and nonlinear optimization, which is of great importance in many scientific areas such as image and signal proce…
compressed sensingModel SelectionMultiobjective OptimizationKinetics-Informed Neural Networks
Chemical kinetics and reaction engineering consists of the phenomenological framework for the disentanglement of reaction mechanisms, optimization of reaction performance and the rational design of chemical processes. He…
DisentanglementMultiobjective OptimizationRobust path-following control design of heavy vehicles based on multiobjective evolutionary optimization
The ability to deal with systems parametric uncertainties is an essential issue for heavy self-driving vehicles in unconfined environments. In this sense, robust controllers prove to be efficient for autonomous navigatio…
Autonomous NavigationMultiobjective OptimizationControllable Pareto Multi-Task Learning
A multi-task learning (MTL) system aims at solving multiple related tasks at the same time. With a fixed model capacity, the tasks would be conflicted with each other, and the system usually has to make a trade-off among…
Multiobjective OptimizationMulti-Task LearningInverse Multiobjective Optimization Through Online Learning
We study the problem of learning the objective functions or constraints of a multiobjective decision making model, based on a set of sequentially arrived decisions. In particular, these decisions might not be exact and p…
Decision MakingMultiobjective OptimizationLearning the Pareto Front with Hypernetworks
Multi-objective optimization (MOO) problems are prevalent in machine learning. These problems have a set of optimal solutions, called the Pareto front, where each point on the front represents a different trade-off betwe…
FairnessMultiobjective OptimizationMulti-target regressionMulti-Task Learning+1Wasserstein Distributionally Robust Inverse Multiobjective Optimization
Inverse multiobjective optimization provides a general framework for the unsupervised learning task of inferring parameters of a multiobjective decision making problem (DMP), based on a set of observed decisions from the…
Decision MakingMultiobjective OptimizationPortfolio OptimizationEfficient and Sparse Neural Networks by Pruning Weights in a Multiobjective Learning Approach
Overparameterization and overfitting are common concerns when designing and training deep neural networks, that are often counteracted by pruning and regularization strategies. However, these strategies remain secondary …
Multiobjective OptimizationBalancing Common Treatment and Epidemic Control in Medical Procurement during COVID-19: Transform-and-Divide Evolutionary Optimization
Balancing common disease treatment and epidemic control is a key objective of medical supplies procurement in hospitals during a pandemic such as COVID-19. This problem can be formulated as a bi-objective optimization pr…
Evolutionary AlgorithmsMultiobjective OptimizationA Generative Machine Learning-Based Approach for Inverse Design of Multilayer Metasurfaces
The synthesis of a metasurface exhibiting a specific set of desired scattering properties is a time-consuming and resource-demanding process, which conventionally relies on many cycles of full-wave simulations. It requir…
BIG-bench Machine LearningMultiobjective Optimization