Papers Multiobjective Optimization
“Multiobjective Optimization” 태그가 달린 논문 155편 · 필터 해제
A fast balance optimization approach for charging enhancement of lithium-ion battery packs through deep reinforcement learning
This paper presents an innovative strategy that utilizes reinforcement learning to enhance the fast balance charging of lithium-ion battery packs. We develop an interactive framework for lithium-ion batteries by utilizin…
Deep Reinforcement Learningenergy managementMultiobjective OptimizationReinforcement Learning (RL)Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach
Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage the advanced search algorithms for multiobjective optimization in DeepHyper, a scalable …
Efficient ExplorationHyperparameter OptimizationMultiobjective OptimizationGPU-accelerated Evolutionary Multiobjective Optimization Using Tensorized RVEA
Evolutionary multiobjective optimization has witnessed remarkable progress during the past decades. However, existing algorithms often encounter computational challenges in large-scale scenarios, primarily attributed to …
GPUMultiobjective OptimizationApproximation of a Pareto Set Segment Using a Linear Model with Sharing Variables
In many real-world applications, the Pareto Set (PS) of a continuous multiobjective optimization problem can be a piecewise continuous manifold. A decision maker may want to find a solution set that approximates a small …
Multiobjective OptimizationDevelopment of a machine learning-based design optimization method for crashworthiness analysis
This article investigates design optimisation in the automotive field using machine learning (ML). A thin-walled crash box under axial impact is studied and the design parameters are optimised for front-impact crash test…
Crashworthiness DesignGenerative Adversarial NetworkMultiobjective OptimizationMORBDD: Multiobjective Restricted Binary Decision Diagrams by Learning to Sparsify
In multicriteria decision-making, a user seeks a set of non-dominated solutions to a (constrained) multiobjective optimization problem, the so-called Pareto frontier. In this work, we seek to bring a state-of-the-art met…
Decision MakingMultiobjective OptimizationUMOEA/D: A Multiobjective Evolutionary Algorithm for Uniform Pareto Objectives based on Decomposition
Multiobjective optimization (MOO) is prevalent in numerous applications, in which a Pareto front (PF) is constructed to display optima under various preferences. Previous methods commonly utilize the set of Pareto object…
DiversityMultiobjective OptimizationEnd-to-End Learning for Fair Multiobjective Optimization Under Uncertainty
Many decision processes in artificial intelligence and operations research are modeled by parametric optimization problems whose defining parameters are unknown and must be inferred from observable data. The Predict-Then…
FairnessMultiobjective OptimizationEffective anytime algorithm for multiobjective combinatorial optimization problems
In multiobjective optimization, the result of an optimization algorithm is a set of efficient solutions from which the decision maker selects one. It is common that not all the efficient solutions can be computed in a sh…
Combinatorial OptimizationMultiobjective OptimizationDiffusion Model-Based Multiobjective Optimization for Gasoline Blending Scheduling
Gasoline blending scheduling uses resource allocation and operation sequencing to meet a refinery's production requirements. The presence of nonlinearity, integer constraints, and a large number of decision variables add…
Evolutionary AlgorithmsMultiobjective OptimizationSchedulingBenchmark for CEC 2024 Competition on Multiparty Multiobjective Optimization
The competition focuses on Multiparty Multiobjective Optimization Problems (MPMOPs), where multiple decision makers have conflicting objectives, as seen in applications like UAV path planning. Despite their importance, M…
Multiobjective OptimizationOptimization-based motion primitive automata for autonomous driving
Trajectory planning for autonomous cars can be addressed by primitive-based methods, which encode nonlinear dynamical system behavior into automata. In this paper, we focus on optimal trajectory planning. Since, typicall…
Autonomous DrivingMultiobjective OptimizationTrajectory PlanningExplainable Bayesian Optimization
Manual parameter tuning of cyber-physical systems is a common practice, but it is labor-intensive. Bayesian Optimization (BO) offers an automated alternative, yet its black-box nature reduces trust and limits human-BO co…
Bayesian OptimizationHyperparameter OptimizationMultiobjective OptimizationMultiobjective Optimization Analysis for Finding Infrastructure-as-Code Deployment Configurations
Multiobjective optimization is a hot topic in the artificial intelligence and operations research communities. The design and development of multiobjective methods is a frequent task for researchers and practitioners. As…
Multiobjective OptimizationBayesian Inverse Transfer in Evolutionary Multiobjective Optimization
Transfer optimization enables data-efficient optimization of a target task by leveraging experiential priors from related source tasks. This is especially useful in multiobjective optimization settings where a set of tra…
Bayesian OptimizationMultiobjective OptimizationTransfer LearningA Simulated Annealing-Based Multiobjective Optimization Algorithm for Minimum Weight Minimum Connected Dominating Set Problem
Minimum connected dominating set problem is an NP-hard combinatorial optimization problem in graph theory. Finding connected dominating set is of high interest in various domains such as wireless sensor networks, optical…
Combinatorial OptimizationMultiobjective OptimizationDealing with Structure Constraints in Evolutionary Pareto Set Learning
In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs) have been proposed to find a finite set of approximate Pareto solutions for a given problem in a single run, each with its own str…
Multiobjective OptimizationStochastic OptimizationqPOTS: Efficient batch multiobjective Bayesian optimization via Pareto optimal Thompson sampling
Classical evolutionary approaches for multiobjective optimization are quite accurate but incur a lot of queries to the objectives; this can be prohibitive when objectives are expensive oracles. A sample-efficient approac…
Bayesian OptimizationComputational EfficiencyMultiobjective OptimizationThompson SamplingLarge Language Model for Multi-objective Evolutionary Optimization
Multiobjective evolutionary algorithms (MOEAs) are major methods for solving multiobjective optimization problems (MOPs). Many MOEAs have been proposed in the past decades, of which the search operators need a carefully …
Evolutionary AlgorithmsLanguage ModelingLanguage ModellingLarge Language Model+3Predictive Maintenance Model Based on Anomaly Detection in Induction Motors: A Machine Learning Approach Using Real-Time IoT Data
With the support of Internet of Things (IoT) devices, it is possible to acquire data from degradation phenomena and design data-driven models to perform anomaly detection in industrial equipment. This approach not only i…
Anomaly DetectionMultiobjective Optimization