Sökning: "pareto optimering."
Visar resultat 1 - 5 av 12 uppsatser innehållade orden pareto optimering..
1. Design space exploration for co-mapping of periodic and streaming applications in a shared platform
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : As embedded systems advance, the complexity and multifaceted requirements of products have increased significantly. A trend in this domain is the selection of different types of application models and multiprocessors as the platform. LÄS MER
2. Boost Converter Inductor Design for High-Power Fuel Cells Using Pareto Optimisation for Single and Coupled Cores
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Hydrogen has been identified by the European Commission to be a competitive alternative to fossil fuels within the transport sector in the medium to long perspective. Because of this, an increased understanding is required of the electrical power train present within such vehicles. LÄS MER
3. The economic and environmental impacts of transportation decisions : A multi-objective optimization
Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistikSammanfattning : Getinge AB is a global medical technology company. This master’s thesis is based on the outflow of capital equipments from Getinge’s factory in Växjö to four different sales and service units. LÄS MER
4. Simulation-Based Portfolio Optimization with Coherent Distortion Risk Measures
Master-uppsats, KTH/Matematisk statistikSammanfattning : This master's thesis studies portfolio optimization using linear programming algorithms. The contribution of this thesis is an extension of the convex framework for portfolio optimization with Conditional Value-at-Risk, introduced by Rockafeller and Uryasev. LÄS MER
5. Machine learning multicriteria optimization in radiation therapy treatment planning
Master-uppsats, KTH/Matematisk statistikSammanfattning : In radiation therapy treatment planning, recent works have used machine learning based on historically delivered plans to automate the process of producing clinically acceptable plans. Compared to traditional approaches such as repeated weighted-sum optimization or multicriteria optimization (MCO), automated planning methods have, in general, the benefits of low computational times and minimal user interaction, but on the other hand lack the flexibility associated with general-purpose frameworks such as MCO. LÄS MER