Intensity modulated radiation therapy (IMRT) is a cancer treatment method in which the intensities of the radiation beams are modulated; therefore these beams have non-uniform radiation intensities. The overall result is the delivery of the prescribed dose in the target volume. The dose distribution is conformal to the shape of the target and minimizes the dose to the nearby critical organs. An inverse planning algorithm is used to obtain those non-uniform beam intensities. In inverse treatment planning, the treatment plan is achieved by using an optimization process. The optimized plan results to a high-quality dose distribution in the planning target volume (PTV), which receives the prescribed dose while the dose that is received by the organs at risk (OARs) is reduced. Accordingly, an objective function has to be defined for the PTV, while some constraints have to be considered to handle the dose limitations for the OARs. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

by Cheung, Shing Kwong. / Thesis (Ph.D.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (p. 137-143). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Reliability worth analysis is an important tool for distribution systems planning and operations. The interruption cost model used in the analysis directly affects the accuracy of the reliability worth evaluation. In this dissertation, the reliability worth analysis was dealt with two interruption cost models including an average or aggregated model (AAM), and a probabilistic distribution model (PDM) in two phases. In the first phase, the dissertation presents a reliability cost model based AAM for distribution system planning. The reliability cost model has been derived as a linear function of line flows for evaluating the outages. The objective is to minimize the total cost including the outage cost, feeder resistive loss, and fixed investment cost. The Evolutionary Programming (EP) was used to solve the very complicated mixed-integer, highly non-linear, and non-differential problem. A real distribution network was modeled as the sample system for tests. There is also a higher opportunity to obtain the global optimum during the EP process. In the second phase, the interruption cost model PDM was proposed by using the radial basis function (RBF) neural network with orthogonal least-squares (OLS) learning method. The residential and industrial interruption costs in PDM were integrated by the proposed neural network technique. A Monte-Carlo time sequential simulation technique was adopted for worth assessment. The technique is tested by evaluating the reliability worth of a Taipower system for the installation of disconnected switches, lateral fuses, transformers and alternative supplies. The results show that the two cost models result in very different interruption costs, and PDM may be more realistic in modeling the system.

Thesis (Ph.D.(Computer Science))--University of Pretoria, 2009. / Abstract in English. Includes bibliographical references.

Thesis (M.Sc)--Athabasca University, 2004. / Includes bibliographical references (leaves 31-32). Also available online.

Orientadores: Akebo Yamakami, Fabricio Oliveira de França / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-21T20:23:34Z (GMT). No. of bitstreams: 1 Rampazzo_PriscilaCristinaBerbert_D.pdf: 3129429 bytes, checksum: 7249077a4a6738637b25e26cc8b8c0d6 (MD5) Previous issue date: 2012 / Resumo: O Planejamento da Operação de Sistemas Hidrelétricos é um problema de otimização de grande porte, dinâmico, estocástico, interconectado e não-linear. Várias funções objetivo podem ser utilizadas na representação dos diferentes aspectos do problema. Neste trabalho foram propostas quatro abordagens para a resolução e estudo do problema. As propostas são baseadas em duas Metaheurísticas Evolutivas - Algoritmos Genéticos e Evolução Diferencial - e consideram o problema com as formulações Monobjetivo e Multiobjetivo. Os métodos trabalham simultaneamente com um conjunto de soluções, realizando exploração e explotação do espaço de busca. Com foco principal na Otimização Multiobjetivo, o intuito é encontrar um conjunto de soluções, obtidas em uma única rodada do algoritmo, que possam agregar explicitamente os diferentes critérios do problema. Os algoritmos propostos foram aplicados em vários testes com usinas pertencentes ao Sistema Hidrelétrico Brasileiro. Os resultados obtidos indicam que as abordagens propostas podem ser efetivamente aplicadas ao problema de Planejamento Hidrelétrico, fornecendo soluções alternativas e eficientes. Este trabalho é uma contribuição tanto para o Problema de Planejamento Hidrelétrico, com a proposição de métodos de resolução que permitem a análise de vários aspectos do problema, quanto para a Computação Evolutiva, com a aplicação das técnicas em um problema importante e real / Abstract: The Operation Planning of Hydroelectric Systems is a large, dynamic, stochastic, interconnected and nonlinear optimization problem. Several objective functions can be used in the representation of different aspects of the problem. In this work we proposed four approaches for the study and resolution of the problem. The proposals are based on two Evolutionary Metaheuristics - Genetic Algorithms and Differential Evolution - and consider the problem with single and multiobjective formulations. The methods work simultaneously with a set of solutions in order to perform exploration and exploitation of the search space. With main focus on Multiobjective Optimization, the intent is to find a set of solutions, obtained in a single round of the algorithm, which can explicitly add the different criteria of the problem. The proposed algorithms were applied to several tests with plants belonging to the Brazilian Hydropower System. The achieved results indicate that the proposed approaches can be effectively applied to the Hydropower Planning, providing efficient and alternative solutions. This work is a contribution so much to the Problem of Hydropower Planning, with the proposition of resolution methods that allow the analysis of various aspects of the problem, as for the Evolutionary Computation, with the application of the techniques in a real and important problem / Doutorado / Automação / Doutor em Engenharia Elétrica

Abstract not available

Abstract This dissertation presents a new algorithm by integrating evolutionary programming (EP), tabu search (TS) and quadratic programming (QP), named the evolutionary-tabu quadratic programming (ETQ) method, to solve the nonconvex economic dispatch problem (NED). This problem involves the economic dispatch with valve-point effects (EDVP), economic dispatch with piecewise quadratic cost function (EDPQ), and economic dispatch with prohibited operating zones (EDPO). EDPV, EDPQ and EDPO are similar problems when ETQ was employed. The problem was solved in two phases, the cost-curve-selection subproblem, and the typical ED solving subproblem. The first phase was resolved by using a hybrid EP and TS, and the second phase by QP. In the solving process, EP with repairing strategy was used to generate feasible solutions, TS was used to prevent prematurity, and QP was used to enhance the performance. Numerical results show that the proposed method is more effective than other previously developed evolutionary computation algorithms.

Strategic behaviors in electricity markets are analyzed. Three related topics are investigated. The first topic is a research about the NE search algorithm for complex non-cooperative games in electricity markets with transmission constraints. Hybrid co-evolutionary programming is suggested and simulated for complex examples. The second topic is an analysis about the competing pricing mechanisms of uniform and pay-as-bid pricing in an electricity market. We prove that for a two-player static game the Nash Equilibrium under pay-as-bid pricing will yield less total revenue in expectation than under uniform pricing when demand is inelastic. The third topic is to address a market power mitigation issue of the current Texas electricity market by limiting Transmission Congestion Right (TCR) ownership. The strategic coordination of inter zonal scheduling and balancing market manipulation is analyzed. A market power measurement algorithm useful to determine the proper level of TCR ownership limitation is suggested. / text

Πέραν από τις κλασσικές μεθόδους ηλεκτρονικής μάθησης που εφαρμόζονται σήμερα, προτείνεται ένας συνδυασμός εξελικτικών αλγορίθμων και τεχνητής νοημοσύνης για την δημιουργία έξυπνων προσαρμοστικών συστημάτων ηλεκτρονικής μάθησης. Σε αυτή τη διπλωματική εργασία περιγράφονται και παρουσιάζονται οι προτεινόμενοι αλγόριθμοι και ταυτόχρονα η προτεινόμενη πιλοτική εφαρμογή. Το προτεινόμενο σύστημα μπορεί να προβλέψει τις μαθησιακές ικανότητες του μαθητή, μέσα από εξεταστικές διαδικασίες οι οποίες προσφέρονται από το σύστημα, με αποτέλεσμα, το σύστημα να είναι σε θέση να προβλέψει τις επόμενες κινήσεις του μαθητή. Μέσα από την προτεινόμενη εφαρμογή αναπτύχθηκαν μηχανισμοί οι οποίοι συλλέγουν πληροφορίες για τον κάθε χρήστη ξεχωριστά και δημιουργούν ανεξάρτητα προφίλ χρήστη για τον κάθε ένα. Με την χρήση συνδυασμού εξελικτικών αλγορίθμων και αλγορίθμων μάθησης το σύστημα εκπαιδεύεται ώστε να μπορεί να προβλέπει τις μελλοντικές κινήσεις του χρήστη. Η εφαρμογή που αναπτύχτηκε είναι βασισμένη σε τεχνολογίες διαδικτύου, βάσεις δεδομένων και τεχνολογίες έξυπνων πρακτόρων. / As a step beyond the classic e-learning methods that are applied today, the combination of evolutionary programming with artificial intelligence has incorporated in order to create an intelligent adaptive e-learning system. In this thesis the theory of the proposed algorithms are presented and the proposed pilot application too. The proposed system can predict the learning possibilities of a student, concerning the knowledge that is provided to him by the system, thus providing the ability to the machine to predict and anticipate his reactions. We have developed applications that can collect information for the student’s history, thus creating concrete individual profiles. Then, using evolutionary programming techniques combined with machine learning algorithms the system is trained in order to can henceforth calculate and anticipate the student’s knowledge. The applications that have been developed are based on internet technologies, data bases and intelligent agents’ technology.