Search results for "optimointi"

Time-Dependent Multiple Depot Vehicle Routing Problem on Megapolis Network under Wardrop's Traffic Flow Assignment

2018

In this work multiple depot vehicle routing problem is considered in case of variable travel times between nodes on a metropolis network. This variant of the classic multiple depot vehicle routing problem is motivated by the fact that in urban contexts variable traffic conditions play an essential role and can not be ignored in order to perform a realistic optimization. Time-travel matrices corresponding to each period of planning horizon were formed by solving the traffic assignment problem in conjunction with shortest path problem. Routing problem instances include from 20 to 100 customers randomly chosen from a road network of Saint-Petersburg. The results demonstrate that taking into ac…

Simuloidun jäähdytyksen suppenemislause

2013

Tämä pro gradu -tutkielma käsittelee simuloitu jäähdytys -nimisen kombinatorisen optimointimenetelmän teoriaa ja käytäntöä. Esimerkiksi kuvankäsittelyssä sovelletun algoritmin ideana on löytää annetulla joukolla määritellyn reaaliarvoisen energiafunktion globaali minimikohta sallimalla - ei pelkästään energiaa vähentäviä - vaan myös energiaa kasvattavia siirtymiä lähtöjoukon alkioiden välillä. Tilastolliseen fysiikkaan analogian omaavan, Gibbsin jakauman ominaisuuksiin pohjautuvan menetelm än matemaattisena perustana toimivat epähomogeeniset Markovin ketjut, joiden suppenemista tarkastellaan Dobrushinin kontraktiokerroinmenetelmän avulla. Simuloidun jäähdytyksen suppenemislause, joka takaa …

Metaheuristics meet metamodels : a modeling language and a product line architecture for route optimization systems

2011

Cooperative spectrum sensing schemes for future dynamic spectrum access infrastructures

2016

Mechanics of Moving Materials

2014

Korppi-opintotietojärjestelmän tietokantakyselyiden nopeuttaminen

2008

Automatic surrogate modelling technique selection based on features of optimization problems

2019

A typical scenario when solving industrial single or multiobjective optimization problems is that no explicit formulation of the problem is available. Instead, a dataset containing vectors of decision variables together with their objective function value(s) is given and a surrogate model (or metamodel) is build from the data and used for optimization and decision-making. This data-driven optimization process strongly depends on the ability of the surrogate model to predict the objective value of decision variables not present in the original dataset. Therefore, the choice of surrogate modelling technique is crucial. While many surrogate modelling techniques have been discussed in the liter…

A variational inequality approach to constrained control problems

1986

Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system

2017

We tackle three different challenges in solving a real-world industrial problem: formulating the optimization problem, connecting different simulation tools and dealing with computationally expensive objective functions. The problem to be optimized is an air intake ventilation system of a tractor and consists of three computationally expensive objective functions. We describe the modeling of the system and its numerical evaluation with a commercial software. To obtain solutions in few function evaluations, a recently proposed surrogate-assisted evolutionary algorithm K-RVEA is applied. The diameters of four different outlets of the ventilation system are considered as decision variables. Fr…

Exact extension of the DIRECT algorithm to multiple objectives

2019

The direct algorithm has been recognized as an efficient global optimization method which has few requirements of regularity and has proven to be globally convergent in general cases. direct has been an inspiration or has been used as a component for many multiobjective optimization algorithms. We propose an exact and as genuine as possible extension of the direct method for multiple objectives, providing a proof of global convergence (i.e., a guarantee that in an infinite time the algorithm becomes everywhere dense). We test the efficiency of the algorithm on a nonlinear and nonconvex vector function. peerReviewed