Papers Multiobjective Optimization
“Multiobjective Optimization” 태그가 달린 논문 155편 · 필터 해제
Evaluating the Efficacy of LLM-Based Reasoning for Multiobjective HPC Job Scheduling
High-Performance Computing (HPC) job scheduling involves balancing conflicting objectives such as minimizing makespan, reducing wait times, optimizing resource use, and ensuring fairness. Traditional methods, including h…
Computational EfficiencyFairnessLarge Language ModelMultiobjective Optimization+1iDSE: Navigating Design Space Exploration in High-Level Synthesis Using LLMs
High-Level Synthesis (HLS) serves as an agile hardware development tool that streamlines the circuit design by abstracting the register transfer level into behavioral descriptions, while allowing designers to customize t…
High-Level SynthesisMultiobjective OptimizationNavigateClustering-Based Evolutionary Federated Multiobjective Optimization and Learning
Federated learning enables decentralized model training while preserving data privacy, yet it faces challenges in balancing communication efficiency, model performance, and privacy protection. To address these trade-offs…
ClusteringDiversityFederated LearningMultiobjective Optimization+1Bridging Evolutionary Multiobjective Optimization and GPU Acceleration via Tensorization
Evolutionary multiobjective optimization (EMO) has made significant strides over the past two decades. However, as problem scales and complexities increase, traditional EMO algorithms face substantial performance limitat…
CPUGPUMultiobjective OptimizationRuntime Analysis of Evolutionary Algorithms for Multiparty Multiobjective Optimization
In scenarios where multiple decision-makers operate within a common decision space, each focusing on their own multi-objective optimization problem (e.g., bargaining games), the problem can be modeled as a multi-party mu…
Evolutionary AlgorithmsMultiobjective OptimizationEpoch-based Application of Problem-Aware Operators in a Multiobjective Memetic Algorithm for Portfolio Optimization
We consider the issue of intensification/diversification balance in the context of a memetic algorithm for the multiobjective optimization of investment portfolios with cardinality constraints. We approach this issue in …
Multiobjective OptimizationPortfolio OptimizationA Performance Investigation of Multimodal Multiobjective Optimization Algorithms in Solving Two Types of Real-World Problems
In recent years, multimodal multiobjective optimization algorithms (MMOAs) based on evolutionary computation have been widely studied. However, existing MMOAs are mainly tested on benchmark function sets such as the 2019…
feature selectionMultiobjective OptimizationNon-Dominated Sorting Bidirectional Differential Coevolution
Constrained multiobjective optimization problems (CMOPs) are commonly found in real-world applications. CMOP is a complex problem that needs to satisfy a set of equality or inequality constraints. This paper proposes a v…
Multiobjective OptimizationComparative Analysis of Indicators for Multiobjective Diversity Optimization
Indicator-based (multiobjective) diversity optimization aims at finding a set of near (Pareto-)optimal solutions that maximizes a diversity indicator, where diversity is typically interpreted as the number of essentially…
DiversityEvolutionary AlgorithmsMultiobjective OptimizationPrincipal Orthogonal Latent Components Analysis (POLCA Net)
Representation learning is a pivotal area in the field of machine learning, focusing on the development of methods to automatically discover the representations or features needed for a given task from raw data. Unlike t…
Dimensionality ReductionFeature CorrelationFeature EngineeringMultiobjective Optimization+1Power-Efficient Full-Duplex Satellite Communications Aided by Movable Antennas
This letter investigates a movable antenna (MA)-aided full-duplex (FD) satellite communication system, where the satellite, equipped with both transmit and receive MAs, serves multiple uplink (UL) and downlink (DL) user …
Multiobjective OptimizationLibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorch
Multiobjective optimization problems (MOPs) are prevalent in machine learning, with applications in multi-task learning, learning under fairness or robustness constraints, etc. Instead of reducing multiple objective func…
Evolutionary AlgorithmsFairnessMultiobjective OptimizationMulti-Task LearningPrescribed-time Convergent Distributed Multiobjective Optimization with Dynamic Event-triggered Communication
This paper addresses distributed constrained multiobjective resource allocation problems (DCMRAPs) within multi-agent networks, where each agent has multiple, potentially conflicting local objectives, constrained by both…
Decision MakingDistributed OptimizationMultiobjective OptimizationSustainDC: Benchmarking for Sustainable Data Center Control
Machine learning has driven an exponential increase in computational demand, leading to massive data centers that consume significant amounts of energy and contribute to climate change. This makes sustainable data center…
BenchmarkingManagementMulti-agent Reinforcement LearningMultiobjective Optimization+1Multiobjective Vehicle Routing Optimization with Time Windows: A Hybrid Approach Using Deep Reinforcement Learning and NSGA-II
This paper proposes a weight-aware deep reinforcement learning (WADRL) approach designed to address the multiobjective vehicle routing problem with time windows (MOVRPTW), aiming to use a single deep reinforcement learni…
Deep Reinforcement LearningMultiobjective OptimizationMulti-task multi-constraint differential evolution with elite-guided knowledge transfer for coal mine integrated energy system dispatching
The dispatch optimization of coal mine integrated energy system is challenging due to high dimensionality, strong coupling constraints, and multiobjective. Existing constrained multiobjective evolutionary algorithms stru…
DiversityEvolutionary AlgorithmsMultiobjective OptimizationTransfer LearningAI-Assisted Detector Design for the EIC (AID(2)E)
Artificial Intelligence is poised to transform the design of complex, large-scale detectors like the ePIC at the future Electron Ion Collider. Featuring a central detector with additional detecting systems in the far for…
Multiobjective OptimizationNavigateRobust DesignAn Efficient Approach for Solving Expensive Constrained Multiobjective Optimization Problems
To solve real-world expensive constrained multi-objective optimization problems (ECMOPs), surrogate/approximation models are commonly incorporated in evolutionary algorithms to pre-select promising candidate solutions fo…
Evolutionary AlgorithmsMultiobjective OptimizationLarge Language Model-Aided Evolutionary Search for Constrained Multiobjective Optimization
Evolutionary algorithms excel in solving complex optimization problems, especially those with multiple objectives. However, their stochastic nature can sometimes hinder rapid convergence to the global optima, particularl…
Evolutionary AlgorithmsLanguage ModelingLanguage ModellingLarge Language Model+2Common pitfalls to avoid while using multiobjective optimization in machine learning
Recently, there has been an increasing interest in the application of multiobjective optimization (MOO) in machine learning (ML). This interest is driven by the numerous real-life situations where multiple objectives mus…
Evolutionary AlgorithmsMisconceptionsMultiobjective Optimization