Searched for: subject%3A%22Constrained%255C%2BOptimization%22
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Giraud, Bastien (author)
The transition to green energy is reshaping the energy landscape, marked by increased integration of renewable energy sources, distributed resources, and the electrification of other energy sectors. These changes challenge grid security, particularly regarding the N-1 security criterion, a crucial factor in preventing blackouts. Furthermore,...
master thesis 2023
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Koch, Johannes (author)
The Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) is a state-of-the-art algorithm for single-objective, real-valued optimization. As many practical applications are inherently constrained, evolutionary algorithms are equipped with constraint handling techniques to allow optimizing constrained problems. The approach...
master thesis 2023
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Heijne, Nick (author)
The building industry is responsible for a large amount of CO2 emissions. With an estimated 11.7 GT in 2020, the building industry emitted 36% of the worldwide CO2 emissions (Bertin et al., 2022). This results in the need to efficiently use the current material supply. A way to achieve this is by transitioning from a linear economy to a circular...
master thesis 2023
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Yang, Q. (author)
In traditional reinforcement learning (RL) problems, agents can explore environments to learn optimal policies through trials and errors that are sometimes unsafe. However, unsafe interactions with environments are unacceptable in many safety-critical problems, for instance in robot navigation tasks. Even though RL agents can be trained in...
doctoral thesis 2023
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Yeh, Haoming (author)
During the preoperative planning for breast-conserving surgery, the surgeon makes use of an MRI scan of the breast cancer patient in the prone position to accurately locate the tumour. However, surgery is performed with the patient in the supine position. The surgeon needs to mentally translate the location of the tumour from the prone position...
master thesis 2023
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Leith, Douglas J. (author), Iosifidis, G. (author)
In this paper we extend the classical Follow-The-Regularized-Leader (FTRL) algorithm to encompass time-varying constraints, through adaptive penalization. We establish sufficient conditions for the proposed Penalized FTRL algorithm to achieve O(t) regret and violation with respect to a strong benchmark X^tmax. Lacking prior knowledge of the...
conference paper 2023
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Cheng, J. (author), Lin, Qiao (author), Jiaxiang, Y. (author)
In this paper, a variable-fidelity constrained lower confidence bound (VF-CLCB) criterion is presented for computationally expensive constrained optimization problems (COPs) with two levels of fidelity. In VF-CLCB, the hierarchical Kriging model is adopted to model the objective and inequality constraints. Two infill sampling functions are...
journal article 2022
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Castelli, Mauro (author), Manzoni, Luca (author), Mariot, L. (author), Nobile, Marco S. (author), Tangherloni, Andrea (author)
In the crowded environment of bio-inspired population-based metaheuristics, the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum. Inspired by the peculiar spatial arrangement of salp colonies, which are displaced in long chains following a leader, this algorithm seems to provide an interesting...
review 2022
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Jayachandra, Karan (author)
Due to the rising number of wireless device users, it is expected that there will be a scarcity in the spectrum. The will especially true for the Automotive Spectrum between 77 and 81 GHz. In this thesis, we apply Sensor Management to the Joint Radar Communication scenario. We develop an algorithm that can allocate resources to both sensing and...
master thesis 2021
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Markensteijn, A.S. (author)
Energy systems are vital in modern society, and reliable operation is crucial. Multi-carrier energy systems (MESs), which couple two or more single-carrier systems, have recently become more important, as the need for sustainable energy systems increases. Important tools for the design and operation of energy systems are steady-state simulation...
doctoral thesis 2021
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van der Werk, Bas (author)
The Radar Resource Management (RRM) problem in a multi-sensor multi-target scenario is considered. The problem is defined as a constrained optimization problem in which the predicted error covariance is minimized subject to resource budget constraints. By applying Lagrangian Relaxation (LR) the problem is decoupled into multiple sub-optimization...
master thesis 2021
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Markensteijn, A.S. (author), Romate, J.E. (author), Vuik, Cornelis (author)
Optimization is an important tool for the operation of an energy system. Multi-carrier energy systems (MESs) have recently become more important. Load flow (LF) equations are used within optimization to determine if physical network limits are violated. Due to nonlinearities, the solvability of the OF problem and the convergence of the...
journal article 2021
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Monteil, Jean-Baptiste (author), Iosifidis, G. (author), DaSilva, Luiz (author)
Emerging network slicing markets promise to boost the utilization of expensive network resources and to unleash the potential of over-the-top services. Their success, however, is conditioned on the service providers (SPs) being able to bid effectively for the virtualized resources. In this paper we consider a hybrid advance-reservation and spot...
conference paper 2021
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van der Werk, Bas (author), Schöpe, M.I. (author), Driessen, J.N. (author)
Radar Resource Management in a multi-sensor multi-target scenario is considered. A dynamic resource balancing algorithm is proposed which optimizes target task parameters assuming an underlying partially observable Markov decision process (POMDP). By applying stochastic optimization methods, such as policy rollout, the POMDP is solved non...
conference paper 2021
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de Boer, Thies (author), Schöpe, M.I. (author), Driessen, J.N. (author)
The radar resource management problem in a multi-target tracking scenario is considered. Partially observable Markov decision processes (POMDPs) are used to describe each tracking task. Model predictive control is applied to solve the POMDPs in a non-myopic way. As a result, the computational complexity compared to stochastic optimization...
conference paper 2021
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Maljaars, J.M. (author), Richardson, Chris N. (author), Sime, Nathan (author)
This paper introduces LEOPART, an add-on for the open-source finite element software library FENICS to seamlessly integrate Lagrangian particle functionality with (Eulerian) mesh-based finite element (FE) approaches. LEOPART- which is so much as to say: ‘Lagrangian–Eulerian on Particles’ - contains tools for efficient, accurate and scalable...
journal article 2021
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Baayen, Jorn H. (author), Postek, K.S. (author)
Non-convex discrete-time optimal control problems in, e.g., water or power systems, typically involve a large number of variables related through nonlinear equality constraints. The ideal goal is to find a globally optimal solution, and numerical experience indicates that algorithms aiming for Karush–Kuhn–Tucker points often find solutions...
journal article 2021
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Maljaars, J.M. (author)
This thesis presents a numerical framework for simulating advection-dominated flows which reconciles the advantages of Eulerian mesh-based schemes with those of a Lagrangian particle-based discretization strategy. Particularly, the strategy proposed in this thesis inherits the diffusion-free properties as in Lagrangian particle-based advection,...
doctoral thesis 2019
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Su, Z. (author), Jamshidi, A. (author), Nunez, Alfredo (author), Baldi, S. (author), De Schutter, B.H.K. (author)
We develop a multi-level decision making approach for optimal condition-based maintenance planning of a railway network divided into a large number of sections with independent stochastic deterioration dynamics. At higher level, a chance-constrained Model Predictive Control (MPC) controller determines the long-term section-wise maintenance...
journal article 2019
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Maljaars, J.M. (author), Labeur, R.J. (author), Trask, Nathaniel (author), Sulsky, Deborah (author)
By combining concepts from particle-in-cell (PIC) and hybridized discontinuous Galerkin (HDG) methods, we present a particle–mesh scheme for flow and transport problems which allows for diffusion-free advection while satisfying mass and momentum conservation – locally and globally – and extending to high-order spatial accuracy. This is achieved...
journal article 2019
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