Sökning: "Kombinatorisk Optimering"

Visar resultat 1 - 5 av 8 uppsatser innehållade orden Kombinatorisk Optimering.

1. 1. The Applicability and Scalability of Graph Neural Networks on Combinatorial Optimization

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :Peder Hårderup; [2023]
   Nyckelord :applied mathematics; combinatorial optimization; machine learning; graph neural networks; scalability; tillämpad matematik; kombinatorisk optimering; maskininlärning; grafiska neurala nätverk; skalbarhet;

   Sammanfattning : This master's thesis investigates the application of Graph Neural Networks (GNNs) to address scalability challenges in combinatorial optimization, with a primary focus on the minimum Total Dominating set Problem (TDP) and additionally the related Carrier Scheduling Problem (CSP) in networks of Internet of Things. The research identifies the NP-hard nature of these problems as a fundamental challenge and addresses how to improve predictions on input graphs of sizes much larger than seen during training phase. LÄS MER

3. 2. Optimization of Physical Uplink Resource Allocation in 5G Cellular Network using Monte Carlo Tree Search

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Gerard Girame Rizzo; [2022]
   Nyckelord :5G NR; PUCCH format; Combinatorial optimization; Physical resource allocation; Monte Carlo Tree Search; 5G NR; PUCCH-format; kombinatorisk optimering; fysisk resursfördelning; Monte Carlo Tree Search;

   Sammanfattning : The Physical Uplink Control Channel (PUCCH), which is mainly used to transmit Uplink Control Information (UCI), is a key component to enable the 5G NR system. Compared to LTE, NR specifies a more flexible PUCCH structure to support various applications and use cases. LÄS MER

4. 3. Investigating Multi-Objective Reinforcement Learning for Combinatorial Optimization and Scheduling Problems : Feature Identification for multi-objective Reinforcement Learning models

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Rikard Fridsén Skogsberg; [2022]
   Nyckelord :Multi-Objective Reinforcement Learning; Radio Resource Scheduling; Deep Q-Networks; Single-policy; Multi-policy; Scalarization.; Flermåls förstärkningsinlärning; Radio resurs schemaläggning; Djupa Q-nätverk; Enskilt mål; Flermål;

   Sammanfattning : Reinforcement Learning (RL) has in recent years become a core method for sequential decision making in complex dynamical systems, being of great interest to support improvements in scheduling problems. This could prove important to areas in the newer generation of cellular networks. LÄS MER

5. 4. Kombinatorisk Optimering med Pointer Networks och Reinforcement Learning

   Master-uppsats, Linköpings universitet/Artificiell intelligens och integrerade datorsystem

   Författare :Axel Holmberg; Wilhelm Hansson; [2021]
   Nyckelord :Pointer Networks; Reinforcement Learning; Combinatorial Optimization;

   Sammanfattning : Given the complexity and range of combinatorial optimization problems, solving them can be computationally easy or hard. There are many ways to solve them, but all available methods share a problem: they take a long time to run and have to be rerun when new cases are introduced. LÄS MER

7. 5. Route Planning of Transfer Buses Using Reinforcement Learning

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Gustav Holst; [2020]
   Nyckelord :Route Planning; Reinforcement Learning; Neural Networks; Transfer Buses; Combinatorial Optimization; Ruttplanering; Förstärkningsinlärning; Neurala Nätverk; Transferbussar; Kombinatorisk Optimering;

   Sammanfattning : In route planning the goal is to obtain the best route between a set of locations, which becomes a very complex task as the number of locations increase. This study will consider the problem of transfer bus route planning and examines the feasibility of applying a reinforcement learning method in this specific real-world context. LÄS MER