Papers Multiobjective Optimization
“Multiobjective Optimization” 태그가 달린 논문 155편 · 필터 해제
Modeling the Material-Inventory Transportation Problem Using Multi-Objective Optimization
In the era of industry 4.0, procurement in supply chain management is the key to developing information management systems. It directly affects production planning failure. In this case, it is the process to prepare and …
ManagementMultiobjective OptimizationAn Effective and Efficient Evolutionary Algorithm for Many-Objective Optimization
In evolutionary multiobjective optimization, effectiveness refers to how an evolutionary algorithm performs in terms of converging its solutions into the Pareto front and also diversifying them over the front. This is no…
Density EstimationEvolutionary AlgorithmsMultiobjective OptimizationBalancing Exploration and Exploitation for Solving Large-scale Multiobjective Optimization via Attention Mechanism
Large-scale multiobjective optimization problems (LSMOPs) refer to optimization problems with multiple conflicting optimization objectives and hundreds or even thousands of decision variables. A key point in solving LSMO…
Evolutionary AlgorithmsMultiobjective OptimizationNondominated-Solution-based Multi-objective Greedy Sensor Selection for Optimal Design of Experiments
In this study, a nondominated-solution-based multi-objective greedy method is proposed and applied to a sensor selection problem based on the multiple indices of the optimal design of experiments. The proposed method sim…
Multiobjective OptimizationD3PG: Dirichlet DDPG for Task Partitioning and Offloading With Constrained Hybrid Action Space in Mobile-Edge Computing
Mobile-edge computing (MEC) has been regarded as a promising paradigm to reduce service latency for data processing in the Internet of Things (IoT) by provisioning computing resources at the network edges. In this work, …
Deep Reinforcement LearningEdge-computingGeneral Reinforcement LearningMultiobjective Optimization+2Solving integer multi-objective optimization problems using TOPSIS, Differential Evolution and Tabu Search
This paper presents a method to solve non-linear integer multiobjective optimization problems. First the problem is formulated using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Next, the …
Multiobjective OptimizationMulti-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems
In this work, a new multiobjective optimization algorithm called multiobjective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of transferring students from high …
DescriptiveMultiobjective OptimizationMulti Agent Reinforcement Learning Trajectory Design and Two-Stage Resource Management in CoMP UAV VLC Networks
In this paper, we consider unmanned aerial vehicles (UAVs) equipped with a visible light communication (VLC) access point and coordinated multipoint (CoMP) capability that allows users to connect to more than one UAV. UA…
ManagementMulti-agent Reinforcement LearningMultiobjective OptimizationDOCKSTRING: easy molecular docking yields better benchmarks for ligand design
The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solubility or general druglikeness, which can…
Drug DesignDrug DiscoveryMolecular DockingMultiobjective Optimization+1A novel multiobjective evolutionary algorithm based on decomposition and multi-reference points strategy
Many real-world optimization problems such as engineering design can be eventually modeled as the corresponding multiobjective optimization problems (MOPs) which must be solved to obtain approximate Pareto optimal fronts…
DiversityEvolutionary AlgorithmsMultiobjective OptimizationA portfolio approach to massively parallel Bayesian optimization
One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayesian optimization have been pr…
Bayesian OptimizationMultiobjective OptimizationBenchmark Problems for CEC2021 Competition on Evolutionary Transfer Multiobjectve Optimization
Evolutionary transfer multiobjective optimization (ETMO) has been becoming a hot research topic in the field of evolutionary computation, which is based on the fact that knowledge learning and transfer across the related…
Multiobjective OptimizationTransfer LearningUsing Traceless Genetic Programming for Solving Multiobjective Optimization Problems
Traceless Genetic Programming (TGP) is a Genetic Programming (GP) variant that is used in cases where the focus is rather the output of the program than the program itself. The main difference between TGP and other GP te…
Multiobjective OptimizationKKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Survey
This is a tutorial and survey paper on Karush-Kuhn-Tucker (KKT) conditions, first-order and second-order numerical optimization, and distributed optimization. After a brief review of history of optimization, we start wit…
Distributed OptimizationMultiobjective OptimizationSecond-order methodsStochastic OptimizationQoS-aware Big Service Composition using Distributed Co-Evolutionary Algorithm
Big services are collections of interrelated web services across virtual and physical domains, processing Big Data. Existing service selection and composition algorithms fail to achieve the global optimum solution in a r…
DiversityMultiobjective OptimizationService CompositionCharacterization of Constrained Continuous Multiobjective Optimization Problems: A Feature Space Perspective
Despite the increasing interest in constrained multiobjective optimization in recent years, constrained multiobjective optimization problems (CMOPs) are still unsatisfactory understood and characterized. For this reason,…
BenchmarkingMultiobjective OptimizationClustering-Based Subset Selection in Evolutionary Multiobjective Optimization
Subset selection is an important component in evolutionary multiobjective optimization (EMO) algorithms. Clustering, as a classic method to group similar data points together, has been used for subset selection in some f…
ClusteringMultiobjective OptimizationOnline Minimax Multiobjective Optimization: Multicalibeating and Other Applications
We introduce a simple but general online learning framework in which a learner plays against an adversary in a vector-valued game that changes every round. Even though the learner's objective is not convex-concave (and s…
Multiobjective OptimizationMODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization
Learning-based heuristics for solving combinatorial optimization problems has recently attracted much academic attention. While most of the existing works only consider the single objective problem with simple constraint…
Combinatorial OptimizationDeep Reinforcement LearningMultiobjective Optimizationreinforcement-learning+1Gridless Evolutionary Approach for Line Spectral Estimation with Unknown Model Order
Gridless methods show great superiority in line spectral estimation. These methods need to solve an atomic $l_0$ norm (i.e., the continuous analog of $l_0$ norm) minimization problem to estimate frequencies and model ord…
Multiobjective Optimization