Search results for "optimointi"

Modeling and Mitigating Errors in Belief Propagation for Distributed Detection

2021

We study the behavior of the belief-propagation (BP) algorithm affected by erroneous data exchange in a wireless sensor network (WSN). The WSN conducts a distributed multidimensional hypothesis test over binary random variables. The joint statistical behavior of the sensor observations is modeled by a Markov random field whose parameters are used to build the BP messages exchanged between the sensing nodes. Through linearization of the BP message-update rule, we analyze the behavior of the resulting erroneous decision variables and derive closed-form relationships that describe the impact of stochastic errors on the performance of the BP algorithm. We then develop a decentralized distribute…

Pilvipalveluiden kustannusoptimointi pienissä ja keskisuurissa yrityksissä

2018

Pilvilaskenta on mullistava teknologia, joka on herättänyt viime vuosina merkittävää huomiota sekä tiede- että yritysmaailmassa. Pilvilaskenta mahdollistaa merkittävät hyödyt erityisesti yrityksille. Pilvilaskennan omaksumisen merkitys korostuu pienten ja keskisuurten yritysten tapauksessa, sillä ne kokevat merkittäviä haasteita liittyen rajoitettuihin resursseihin ja teknologiseen osaamiseen. Pilvilaskennan merkityksen kasvamisen vuoksi myös siihen liittyvien kustannusten optimoimisesta on tullut ajankohtaista ja merkityksellistä. Tässä tutkielmassa tutkitaan, kuinka pk-yritykset pyrkivät optimoimaan pilvipalveluihin liittyviä kustannuksiaan. Lisäksi tutkitaan pk-yritysten pilvipalvelukoke…

On heuristic hybrid methods and structured point sets in global continuous optimization

2004

Heikki Maaranen tutki väitöskirjassaan kuinka globaalin optimoinnin menetelmiä jatkuvien muuttujien tehtäville voidaan parantaa hybridisointia ja strukturaalisia pistejoukkoja käyttämällä. In this work, we concentrate on improving the performance of global methods for continuous optimization via hybridization and the use of structured point sets. Optimization is an important part of solving real-life problems. The problem solving process involves modeling, simulation and optimization of the simulated model, after which the results can be applied into practice, for example, in product manufacturing. Many of the real-life problems can be formulated as global continuous optimization problems. …

Algorithmic issues in computational intelligence optimization: from design to implementation, from implementation to design

2016

The vertiginous technological growth of the last decades has generated a variety of powerful and complex systems. By embedding within modern hardware devices sophisticated software, they allow the solution of complicated tasks. As side effect, the availability of these heterogeneous technologies results into new difficult optimization problems to be faced by researchers in the field. In order to overcome the most common algorithmic issues, occurring in such a variety of possible scenarios, this research has gone through cherry-picked case-studies. A first research study moved from implementation to design considerations. Implementation limitations, such as memory constraints and real-time r…

Suomalaisten indie-pelistudioiden kokemuksia sovelluskauppaoptimoinnista

2017

Suomalaisten mobiilipelien menestys on ollut viime vuosina huikeaa seuratta- vaa. Suurin osa mobiilipeleistä ei kuitenkaan saavuta menestystä, ja yksi tärkeimmistä syistä tähän on se, ettei peli onnistu saavuttamaan menestyksen kannalta tarvittavaa näkyvyyttä sovelluskaupassa. Tässä tutkielmassa keskitytään sovelluskauppaoptimointiin (engl. App Store Optimization), joka tarjoaa mahdollisuuden pelin näkyvyyden parantamiseen ilman merkittävää taloudellista panostusta. Tutkielman yhteydessä tehdyllä kyselytutkimuksella selvitettiin suomalaisten indie-pelistudioiden kokemuksia sovelluskauppaoptimoinnista. Saatujen tulosten perusteella sovelluskauppaoptimointia käyttäneet pelistudiot ovat koke- …

A Systematic Way of Structuring Real-World Multiobjective Optimization Problems

2023

In recent decades, the benefits of applying multiobjective optimization (MOO) methods in real-world applications have rapidly increased. The MOO literature mostly focuses on problem-solving, typically assuming the problem has already been correctly formulated. The necessity of verifying the MOO problem and the potential impacts of having an incorrect problem formulation on the optimization results are not emphasized enough in the literature. However, verification is crucial since the optimization results will not be meaningful without an accurate problem formulation, not to mention the resources spent in the optimization process being wasted. In this paper, we focus on the MOO problem struc…

Optimal Heating of an Indoor Swimming Pool

2020

This work presents the derivation of a model for the heating process of the air of a glass dome, where an indoor swimming pool is located in the bottom of the dome. The problem can be reduced from a three dimensional to a two dimensional one. The main goal is the formulation of a proper optimization problem for computing the optimal heating of the air after a given time. For that, the model of the heating process as a partial differential equation is formulated as well as the optimization problem subject to the time-dependent partial differential equation. This yields the optimal heating of the air under the glass dome such that the desired temperature distribution is attained after a given…

Multi-criteria optimization in industry

2022

Hybrid evolutionary multi-objective optimization with enhanced convergence and diversity

2011

Comparing reference point based interactive multiobjective optimization methods without a human decision maker

2022

AbstractInteractive multiobjective optimization methods have proven promising in solving optimization problems with conflicting objectives since they iteratively incorporate preference information of a decision maker in the search for the most preferred solution. To find the appropriate interactive method for various needs involves analysis of the strengths and weaknesses. However, extensive analysis with human decision makers may be too costly and for that reason, we propose an artificial decision maker to compare a class of popular interactive multiobjective optimization methods, i.e., reference point based methods. Without involving any human decision makers, the artificial decision make…