Dissertations / Theses on the topic 'PSO'
To see the other types of publications on this topic, follow the link: PSO.
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'PSO.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Krézek, Vladimír. "Akcelerace částicových rojů PSO pomocí GPU." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-235471.
Full textAbstract:
This work deals with the PSO technique (Particle Swarm Optimization), which is capable to solve complex problems. This technique can be used for solving complex combinatorial problems (the traveling salesman problem, the tasks of knapsack), design of integrated circuits and antennas, in fields such as biomedicine, robotics, artificial intelligence or finance. Although the PSO algorithm is very efficient, the time required to seek out appropriate solutions for real problems often makes the task intractable. The goal of this work is to accelerate the execution time of this algorithm by the usage of Graphics processors (GPU), which offers higher computing potential while preserving the favorable price and size. The boolean satisfiability problem (SAT) was chosen to verify and benchmark the implementation. As the SAT problem belongs to the class of the NP-complete problems, any reduction of the solution time may broaden the class of tractable problems and bring us new interesting knowledge.
2
Záň, Drahoslav. "Akcelerace částicových rojů PSO pomocí GPU." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2013. http://www.nusl.cz/ntk/nusl-236374.
Full textAbstract:
This thesis deals with a population based stochastic optimization technique PSO (Particle Swarm Optimization) and its acceleration. This simple, but very effective technique is designed for solving difficult multidimensional problems in a wide range of applications. The aim of this work is to develop a parallel implementation of this algorithm with an emphasis on acceleration of finding a solution. For this purpose, a graphics card (GPU) providing massive performance was chosen. To evaluate the benefits of the proposed implementation, a CPU and GPU implementation were created for solving a problem derived from the known NP-hard Knapsack problem. The GPU application shows 5 times average and almost 10 times the maximum speedup of computation compared to an optimized CPU application, which it is based on.
3
Sergio, Anderson Tenório. "Otimização de Reservoir Computing com PSO." Universidade Federal de Pernambuco, 2013. https://repositorio.ufpe.br/handle/123456789/11498.
Full textAbstract:
Submitted by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-03-09T14:34:23Z
No. of bitstreams: 2
Dissertaçao Anderson Sergio.pdf: 1358589 bytes, checksum: fdd2a84a1ce8a69596fa45676bc522e4 (MD5)
license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5)Made available in DSpace on 2015-03-09T14:34:23Z (GMT). No. of bitstreams: 2 Dissertaçao Anderson Sergio.pdf: 1358589 bytes, checksum: fdd2a84a1ce8a69596fa45676bc522e4 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-03-07
Reservoir Computing (RC) é um paradigma de Redes Neurais Artificiais com aplicações importantes no mundo real. RC utiliza arquitetura similar às Redes Neurais Recorrentes para processamento temporal, com a vantagem de não necessitar treinar os pesos da camada intermediária. De uma forma geral, o conceito de RC é baseado na construção de uma rede recorrente de maneira randômica (reservoir), sem alteração dos pesos. Após essa fase, uma função de regressão linear é utilizada para treinar a saída do sistema. A transformação dinâmica não-linear oferecida pelo reservoir é suficiente para que a camada de saída consiga extrair os sinais de saída utilizando um mapeamento linear simples, fazendo com que o treinamento seja consideravelmente mais rápido. Entretanto, assim como as redes neurais convencionais, Reservoir Computing possui alguns problemas. Sua utilização pode ser computacionalmente onerosa, diversos parâmetros influenciam sua eficiência e é improvável que a geração aleatória dos pesos e o treinamento da camada de saída com uma função de regressão linear simples seja a solução ideal para generalizar os dados. O PSO é um algoritmo de otimização que possui algumas vantagens sobre outras técnicas de busca global. Ele possui implementação simples e, em alguns casos, convergência mais rápida e custo computacional menor. Esta dissertação teve o objetivo de investigar a utilização do PSO (e duas de suas extensões – EPUS-PSO e APSO) na tarefa de otimizar os parâmetros globais, arquitetura e pesos do reservoir de um RC, aplicada ao problema de previsão de séries temporais. Os resultados alcançados mostraram que a otimização de Reservoir Computing com PSO, bem como com as suas extensões selecionadas, apresentaram desempenho satisfatório para todas as bases de dados estudadas – séries temporais de benchmark e bases de dados com aplicação em energia eólica. A otimização superou o desempenho de diversos trabalhos na literatura, apresentando-se como uma solução importante para o problema de previsão de séries temporais.
4
Veselý, Filip. "Aplikace optimalizační metody PSO v podnikatelství." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2010. http://www.nusl.cz/ntk/nusl-222445.
Full textAbstract:
This work deals with two optimization problems, traveling salesman problem and cluster analysis. Solution of these optimization problems are applied on INVEA-TECH company needs. It shortly describes questions of optimization and some optimization techniques. Closely deals with swarm intelligence, strictly speaking particle swarm intelligence. Part of this work is recherché of variants of particle swarm optimization algorithm. The second part describes PSO algorithms solving clustering problem and traveling salesman problem and their implementation in Matlab language.
5
Scarpa, Giulia <1991>. "PSO for CVaR-based Portfolio Selection." Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/8970.
Full textAbstract:
In questo elaborato si intende proporre un nuovo modello di selezione di portafogli che utilizza il Conditional Value at Risk come misura del rischio finanziario, servendosi si una metaeuristica che concentra la ricerca della soluzione ottima nell’area più promettente dello spazio delle soluzioni: la Particle Swarm Optimization.
6
Duhain, Julien Georges Omer Louis. "Particle swarm optimisation in dynamically changing environments - an empirical study." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/25875.
Full textAbstract:
Real-world optimisation problems often are of a dynamic nature. Recently, much research has been done to apply particle swarm optimisation (PSO) to dynamic environments (DE). However, these research efforts generally focused on optimising one variation of the PSO algorithm for one type of DE. The aim of this work is to develop a more comprehensive view of PSO for DEs. This thesis studies different schemes of characterising and taxonomising DEs, performance measures used to quantify the performance of optimisation algorithms applied to DEs, various adaptations of PSO to apply PSO to DEs, and the effectiveness of these approaches on different DE types. The standard PSO algorithm has shown limitations when applied to DEs. To overcome these limitations, the standard PSO can be modi ed using personal best reevaluation, change detection and response, diversity maintenance, or swarm sub-division and parallel tracking of optima. To investigate the strengths and weaknesses of these approaches, a representative sample of algorithms, namely, the standard PSO, re-evaluating PSO, reinitialising PSO, atomic PSO (APSO), quantum swarm optimisation (QSO), multi-swarm, and self-adapting multi-swarm (SAMS), are empirically analysed. These algorithms are analysed on a range of DE test cases, and their ability to detect and track optima are evaluated using performance measures designed for DEs. The experiments show that QSO, multi-swarm and reinitialising PSO provide the best results. However, the most effective approach to use depends on the dimensionality, modality and type of the DEs, as well as on the objective of the algorithm. A number of observations are also made regarding the behaviour of the swarms, and the influence of certain control parameters of the algorithms evaluated. CopyrightDissertation (MSc)--University of Pretoria, 2012.
Computer Science
unrestricted
7
Lång, Magnus. "Sound and Complete Reachability Analysis under PSO." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-213286.
Full textAbstract:
Modern multiprocessor systems use weak (relaxed) memory models in order to execute memory sharing multi-threaded code in an efficient manner, but are much harder for programmers to reason about than systems using the sequential consistency memory model. The SB abstraction and its implementation in the Memorax tool allows sound and complete checking of control state reachability under the TSO memory model, used in modern x86 processors. In this paper, I present a formalisation of the PSO memory model using the semantics of the Sun SPARC documentation and an alternate semantic, called Partial Write Serialisation, I conjecture to be equivalent with my formalisation under the control state reachability problem. PWS is proved to be a well-structured system which allows sound and complete reachability analysis. An implementation of PWS is presented as part of the Memorax tool and demonstrated experimentally to be capable of analysing reachability and inferring minimal fence sets on many non-trivial and real world examples in reasonable time and memory usage.
8
Němeček, Patrik. "Optimalizační úlohy na bázi částicových hejn (PSO)." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236036.
Full textAbstract:
This work deals with particle swarm optimization. The theoretic part briefly describes the problem of optimization. The considerable part focuses on the overall description of particle swarm optimization (PSO). The principle, behavior, parameters, structure and modifications of PSO are described. The next part of the work is a recherché of variants of PSO, including hybridizations of PSO. In practical part the dynamic problems are analyzed and new designed algorithm for dynamic problems AHPSO is described (what it is based on, what was inspired, what elements are used and why). Algorithm is executed on the set of tasks (Moving peaks benchmark) and compared with the best publicly available variants of algorithm PSO on dynamic problems so far.
9
Almasiri, osamah A. "SKIN CANCER DETECTION USING SVM-BASED CLASSIFICATION AND PSO FOR SEGMENTATION." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5489.
Full textAbstract:
Various techniques are developed for detecting skin cancer. However, the type of maligned skin cancer is still an open problem. The objective of this study is to diagnose melanoma through design and implementation of a computerized image analysis system. The dataset which is used with the proposed system is Hospital Pedro Hispano (PH²).
The proposed system begins with preprocessing of images of skin cancer. Then, particle swarm optimization (PSO) is used for detecting the region of interest (ROI). After that, features extraction (geometric, color, and texture) is taken from (ROI). Lastly, features selection and classification are done using a support vector machine (SVM).
Results showed that with a data set of 200 images, the sensitivity (SE) and the specificity (SP) reached 100% with a maximum processing time of 0.03 sec.
10
Palangpour, Parviz Michael. "FFGA implementation of PSO algorithm and neural networks." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2010. http://scholarsmine.mst.edu/thesis/pdf/Palangpour_09007dcc8078a58e.pdf.
Full textAbstract:
Thesis (M.S.)--Missouri University of Science and Technology, 2010.Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed April 8, 2010) Includes bibliographical references (p. 76-78).
11
Jiang, Siyu. "A Comparison of PSO, GA and PSO-GA Hybrid Algorithms for Model-based Fuel Economy Optimization of a Hybrid-Electric Vehicle." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu156612591067731.
Full text
12
Melo, Leonardo Alves Moreira de. "Comparação de algoritmos de enxame de partículas para otimização de problemas em larga escala." Universidade Federal de Goiás, 2018. http://repositorio.bc.ufg.br/tede/handle/tede/9108.
Full textAbstract:
Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2018-11-29T10:40:19Z
No. of bitstreams: 2
Dissertação - Leonardo Alves Moreira de Melo - 2018.pdf: 2693689 bytes, checksum: 850fbad5a82099825d2478ba3415dcac (MD5)
license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2018-11-29T11:09:58Z (GMT) No. of bitstreams: 2 Dissertação - Leonardo Alves Moreira de Melo - 2018.pdf: 2693689 bytes, checksum: 850fbad5a82099825d2478ba3415dcac (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)
Made available in DSpace on 2018-11-29T11:09:58Z (GMT). No. of bitstreams: 2 Dissertação - Leonardo Alves Moreira de Melo - 2018.pdf: 2693689 bytes, checksum: 850fbad5a82099825d2478ba3415dcac (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-10-26
Fundação de Amparo à Pesquisa do Estado de Goiás - FAPEG
In order to address an issue concerning the increasing number of algorithms based on particle swarm optimization (PSO) applied to solve large-scale optimization problems (up to 2000 variables), this article presents analysis and comparisons among five state- of-the-art PSO algorithms (CCPSO2, LSS- PSO, OBL-PSO, SPSO and VCPSO). Tests were performed to illustrate the e ciency and feasibility of using the algorithms for this type of problem. Six benchmark functions most commonly used in the literature (Ackley 1, Griewank, Rastrigin, Rosenbrock, Schwefel 1.2 and Sphere) were tested. The experiments were performed using a high-dimensional problem (500 variables), varying the number of particles (50, 100 and 200 particles) in each algorithm, thus increasing the computational complexity. The analysis showed that the CCPSO2 and OBL-PSO algorithms found significantly better solutions than the other algorithms for more complex multimodal problems (which most resemble realworld problems). However, considering unimodal functions, the CCPSO2 algorithm stood out before the others. Our results and experimental analysis suggest that CCPSO2 and OBL- PSO seem to be highly competitive optimization algorithms to solve complex and multimodal optimization problems.
O número de algoritmos baseados na otimização por enxame de partículas (PSO) aplicados para resolver problemas de otimização em grande escala (até 2.000 variáveis) aumentou significativamente. Este trabalho apresenta análises e comparações entre cinco algoritmos (CCPSO2, LSSPSO, OBL-CPSO, SPSO e VCPSO). Testes foram realizados para ilustrar a eficiência e viabilidade de usar os algoritmos para resolver problemas em larga escala. Seis funções de referência que são comumente utilizadas na literatura (Ackley 1, Griewank, Rastrigin, Rosenbrock, Schwefel 1.2 e Sphere) foram utilizadas para testar a performancedesses algoritmos. Os experimentos foram realizados utilizando um problema de alta dimensionalidade (500 variáveis), variando o número de partículas (50, 100 e 200 partículas) em cada algoritmo, aumentando assim a complexidade computacional. A análise mostrou que os algoritmos CCPSO2 e OBL-CPSO mostraram-se significativamente melhores que os outros algoritmos para problemas multimodais mais complexos (que mais se assemelham a problemas reais). No entanto, considerando as funções unimodais, o algoritmo CCPSO2 destacou-se perante os demais. Nossos resultados e análises experimentais sugerem que o CCPSO2 e o OBL-CPSO são algoritmos de otimização altamente competitivos para resolver problemas de otimização complexos e multimodais em larga escala.
13
Urade, Hemlata S., and Rahila Patel. "Performance Evaluation of Dynamic Particle Swarm Optimization." IJCSN, 2012. http://hdl.handle.net/10150/283597.
Full textAbstract:
Optimization has been an active area of research for
several decades. As many real-world optimization
problems become increasingly complex, better
optimization algorithms are always needed.
Unconstrained optimization problems can be formulated
as a D-dimensional minimization problem as follows:
Min f (x) x=[x1+x2+……..xD]
where D is the number of the parameters to be optimized.
subjected to: Gi(x) <=0, i=1…q
Hj(x) =0, j=q+1,……m
Xε [Xmin, Xmax]D, q is the number of inequality
constraints and m-q is the number of equality constraints.
The particle swarm optimizer (PSO) is a relatively new
technique. Particle swarm optimizer (PSO), introduced by
Kennedy and Eberhart in 1995, [1] emulates flocking
behavior of birds to solve the optimization problems.In this paper the concept of dynamic particle swarm optimization is introduced. The dynamic PSO is different from the existing PSO’s and some local version of PSO in terms of swarm size and topology. Experiment conducted for benchmark functions of single objective optimization problem, which shows the better performance rather the basic PSO. The paper also contains the comparative analysis for Simple PSO and Dynamic PSO which shows the better result for dynamic PSO rather than simple PSO.
14
LIMA, Natália Flora De. "Frankenstein PSO na definição das arquiteturas e ajustes dos pesos e uso de PSO heterogêneo no treinamento de redes neurais feed-forward." Universidade Federal de Pernambuco, 2011. https://repositorio.ufpe.br/handle/123456789/17738.
Full textAbstract:
Submitted by Irene Nascimento (irene.kessia@ufpe.br) on 2016-08-24T17:35:05Z
No. of bitstreams: 2
license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5)
Dissertacao-Natalia_Flora_de_Lima.pdf: 2000980 bytes, checksum: 107f0691d21b9d94e253d08f06a4fbdd (MD5)Made available in DSpace on 2016-08-24T17:35:05Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Dissertacao-Natalia_Flora_de_Lima.pdf: 2000980 bytes, checksum: 107f0691d21b9d94e253d08f06a4fbdd (MD5) Previous issue date: 2011-08-29
Facepe
Este trabalho apresenta dois novos algoritmos, PSO-FPSO e FPSO-FPSO, para a otimização global de redes neurais MLP (do inglês Multi Layer Perceptron) do tipo feed-forward. O propósito destes algoritmos é otimizar de forma simultânea as arquiteturas e pesos sinápticos, objetivando melhorar a capacidade de generalização da rede neural artificial (RNA). O processo de otimização automática das arquiteturas e pesos de uma rede neural vem recebendo grande atenção na área de aprendizado supervisionado, principalmente em problemas de classificação de padrões. Além dos Algoritmos Genéticos, Busca Tabu, Evolução Diferencial, Recozimento simulado que comumente são empregados no treinamento de redes neurais podemos citar abordagens populacionais como a otimização por colônia de formigas, otimização por colônia de abelhas e otimização por enxame de partículas que vêm sendo largamente utilizadas nesta tarefa. A metodologia utilizada neste trabalho trata da aplicação de dois algoritmos do tipo PSO, sendo empregados na otimização das arquiteturas e na calibração dos pesos das conexões. Nesta abordagem os algoritmos são executados de forma alternada e por um número definido de vezes. Ainda no processo de ajuste dos pesos de uma rede neural MLP foram realizados experimentos com enxame de partículas heterogêneos, que nada mais é que a junção de dois ou mais PSOs de tipos diferentes. Para validar os experimentos com os enxames homogêneos foram utilizadas sete bases de dados para problemas de classificação de padrões, são elas: câncer, diabetes, coração, vidros, cavalos, soja e tireóide. Para os experimentos com enxames heterogêneos foram utilizadas três bases, a saber: câncer, diabetes e coração. O desempenho dos algoritmos foi medido pela média do erro percentual de classificação. Algoritmos da literatura são também considerados. Os resultados mostraram que os algoritmos investigados neste trabalho obtiveram melhor acurácia de classificação quando comparados com os algoritmos da literatura mencionados neste trabalho.
This research presents two new algorithms, PSO-FPSO e FPSO-FPSO, that can be used in feed-forward MLP (Multi Layer Perceptron) neural networks for global optimization. The purpose of these algorithms is to optimize architectures and synaptic weight, at same time, to improve the capacity of generalization from Artificial Neural Network (ANN). The automatic optimization process of neural network’s architectures and weights has received much attention in supervised learning, mainly in pattern classification problems. Besides the Genetic Algorithms, Tabu Search, Differential Evolution, Simulated Annealing that are commonly used in the training of neural networks we can mentioned population approaches such Ant Colony Optimization, Bee Colony Optimization and Particle Swarm Optimization that have been widely used this task. The methodology applied in this research reports the use of two PSO algorithms, used in architecture optimization and connection weight adjust. In this approach the algorithms are performed alternately and by predefined number of times. Still in the process of adjusting the weights of a MLP neural network experiments were performed with swarm of heterogeneous particles, which is nothing more than the joining of two or more different PSOs. To validate the experiments with homogeneous clusters were used seven databases for pattern classification problems, they are: cancer, diabetes, heart, glasses, horses, soy and thyroid. For the experiments with heterogeneous clusters were used three bases, namely cancer, diabetes and heart. The performance of the algorithms was measured by the average percentage of misclassification, literature algorithms are also considered. The results showed that the algorithms investigated in this research had better accuracy rating compared with some published algorithms.
15
Toleikytė, Lina. "PSO sveikatą stiprinančių iniciatyvų įgyvendinimo galimybių Lietuvos ligoninėse tyrimas." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2005. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2005~D_20050608_132323-99989.
Full textAbstract:
Aim of the study - to evaluate the opportunities for implementation of Health Promoting initiatives and International Health Promoting Standards in hospitals of Lithuanian HPH Network.
Objectives: 1. To evaluate managers’ knowledge and attitudes towards implementation of Health Promoting initiatives in hospital. 2. To ascertain structures and personnel which could implement Health Promotion activities at hospital. 3. To compare attitudes towards possibilities of establishment and implementation of Health Promoting initiatives and Health Promoting standards among managers from Kaunas Medical University hospital (KMUH) and hospitals involved in Lithuanian HPH Network.
Methods. The study was carried out in KMUH and hospitals of Lithuanian HPH Network. A standard questionnaire was distributed via internet both to managers (n=33) of all departments in KMUH and those from Lithuanian HPH Network (n=11). The questionnaire included two parts: general (created by author of survey) and special (based on WHO experts’ questionnaire, translated into lithuanian). The response rate was 88%.
Results. The majority of respondents define HP initiatives as a very wide spectrum of activities including health education programs both for staff and patients, support in creating healthy and safe workplace, continuous quality improvement plans and efficient management of financial and human resources. According to the survey, level of awareness of HPH’s aims and goals appeared to be rather low among... [to full text]
16
Moret, Cristina <1992>. "GAs and PSO: two metaheuristic methods for portfolio optimization." Master's Degree Thesis, Università Ca' Foscari Venezia, 2018. http://hdl.handle.net/10579/13319.
Full textAbstract:
The thesis begins with the description of the Markowitz model for optimal portfolio selection. Limitations and improvements of such model are described. In the second chapter the concept of metaheuristics is introduced, focusing on two particular metaheuristics: Genetic Algorithms and Particle Swarm Optimization. These concepts are introduced as alternative optimization methods. In the following chapter a portfolio to optimize is chosen as well as the risk measure to use for the portfolio selection model. In the fourth chapter the two metaheuristics, genetic algorithms and particle swarm optimization, are applied in order to find the optimal portfolio. At the end comparisons between the two methods are provided and conclusions are made.
17
MARINHO, Pedro Rafael Diniz. "Some new families of continuos distributions." Universidade Federal de Pernambuco, 2016. https://repositorio.ufpe.br/handle/123456789/18862.
Full textAbstract:
Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-05-23T12:23:58Z
No. of bitstreams: 2
license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5)
SOME NEW FAMILIES OF CONTINUOUS DISTRIBUTIONS.pdf: 5612905 bytes, checksum: 3fd32464f68606705a4b23070897a8e2 (MD5)Made available in DSpace on 2017-05-23T12:23:58Z (GMT). No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) SOME NEW FAMILIES OF CONTINUOUS DISTRIBUTIONS.pdf: 5612905 bytes, checksum: 3fd32464f68606705a4b23070897a8e2 (MD5) Previous issue date: 2016-06-27
FACEPE
The area of survival analysis is important in Statistics and it is commonly applied in biological sciences, engineering, social sciences, among others. Typically, the time of life or failure can have different interpretations depending on the area of application. For example, the lifetime may mean the life itself of a person, the operating time of equipment until its failure, the time of survival of a patient with a severe disease from the diagnosis, the duration of a social event as a marriage, among other meanings. The time of life or survival time is a positive continuous random variable, which can have constant, monotonic increasing, monotonic decreasing or non-monotonic (for example, in the form of a U) hazard function. In the last decades, several families of probabilistic models have been proposed. These models can be constructed based on some transformation of a parent distribution, commonly already known in the literature. A given linear combination or mixture of G models usually defines a class of probabilistic models having G as a special case. This thesis is composed of independent chapters. The first and last chapters are short chapters that include the introduction and conclusions of the study developed. Two families of distributions, namely the exponentiated logarithmic generated (ELG) class and the geometric Nadarajah-Haghighi (NHG) class are studied. The last one is a composition of the Nadarajah-Haghighi and geometric distributions. Further, we develop a statistical library for the R programming language called the AdequacyModel. This is an improvement of the package that was available on CRAN (Comprehensive R Archive Network) and it is currently in version 2.0.0. The two main functions of the library are the goodness.fit and pso functions. The first function allows to obtain the maximum likelihood estimates (MLEs) of the model parameters and some goodness-of-fit of the fitted probabilistic models. It is possible to choose the method of optimization for maximizing the log-likelihood function. The second function presents the method meta-heuristics global search known as particle swarm optimization (PSO) proposed by Eberhart and Kennedy (1995). Such methodology can be used for obtaining the MLEs necessary for the calculation of some measures of adequacy of the probabilistic models.
A área de análise de sobrevivência é importante na Estatística e é comumente aplicada às ciências biológicas, engenharias, ciências sociais, entre outras. Tipicamente, o tempo de vida ou falha pode ter diferentes interpretações dependendo da área de aplicação. Por exemplo, o tempo de vida pode significar a própria vida de uma pessoa, o tempo de funcionamento de um equipamento até sua falha, o tempo de sobrevivência de um paciente com uma doença grave desde o diagnóstico, a duração de um evento social como um casamento, entre outros significados. O tempo de vida é uma variável aleatória não negativa, que pode ter a função de risco na forma constante, monótona crescente, monótona decrescente ou não monótona (por exemplo, em forma de U). Nas últimas décadas, várias famílias de modelos probabilísticos têm sido propostas. Esses modelos podem ser construídos com base em alguma transformação de uma distribuição padrão, geralmente já conhecida na literatura. Uma dada combinação linear ou mistura de modelos G normalmente define uma classe de modelos probabilísticos tendo G como caso especial. Esta tese é composta de capítulos independentes. O primeiro e último são curtos capítulos que incluem a introdução e as conclusões do estudo desenvolvido. Duas famílias de distribuições, denominadas de classe “exponentiated logarithmic generated” (ELG) e a classe “geometric Nadarajah-Haghighi” (NHG) s˜ao estudadas. A ´ultima ´e uma composi¸c˜ao das distribuições de Nadarajah-Haghighi e geométrica. Além disso, desenvolvemos uma biblioteca estatística para a linguagem de programação R chamada AdequacyModel. Esta é uma melhoria do pacote que foi disponibilizado no CRAN (Comprehensive R Archive Network) e está atualmente na versão 2.0.0. As duas principais funções da biblioteca são as funções goodness.fit e pso. A primeira função permite obter as estimativas de máxima verossimilhança (EMVs) dos parâmetros de um modelo e algumas medidas de bondade de ajuste dos modelos probabilísticos ajustados. E possível escolher o método de otimização para maximizar a função de log-verossimilhan¸ca. A segunda função apresenta o método meta-heurístico de busca global conhecido como Particle Swarm Optimization (PSO) proposto por Eberhart e Kennedy (1995). Algumas metodologias podem ser utilizadas para obtenção das EMVs necessárias para o cálculo de algumas medidas de adequação dos modelos probablísticos ajustados.
18
Gustavsson, Carina, and Ida Lübking. "Feminismens intåg i politiken – Partiers strategier och bemötande av Feministiskt Initiativ." Thesis, Högskolan i Borås, Akademin för vård, arbetsliv och välfärd, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-8852.
Full textAbstract:
Denna uppsats handlar om hur några av de redan etablerade partierna har bemött Feministiskt Initiativ och dess inträde i politiken och partiernas syn på jämställdhet och feminism.Vi har använt oss av kvalitativa metoder i form av intervjuer och datainsamling. Vi har intervjuat partier angående deras ideologi och bemötande samt vilka strategier de har antagit för att bemöta nischpartiet Feministiskt Initiativ. Vi har tittat närmare på Position, salience and ownership theory, PSO-teorin, för att se om partier har använt sig utav de strategier som nämns i teorin. Vi har även studerat hur tillkomsten av Feministiskt Initiativ har påverkat de etablerade partiernas prioriteringar och profilering i frågor om jämställdhet och feminism. Vi fokuserar också på tidigare forskning gällande feminismen.Partier ser annorlunda på feminism och på jämställdheten. Efter att ha intervjuat de utvalda partierna så syns det tydliga kopplingar till PSO-teorin. Vi har också studerat om partierna har satt feminism och jämställdhet högre upp på den politiska agendan sedan Feministiskt Initiativs intåg i politiken.This essay is about how some of the already established parties have responded to the Feminist Initiative and it’s entry into politics and the parties' views on gender equality and feminism.We have used qualitative methods in the form of interviews and data collection. We interviewed the parties regarding their ideology and attitude as well as the strategies they have adopted to address niche party Feminist Initiative. We have looked at Position, salience and ownership, PSO-theory to see if the parties have used out the strategies significantly in theory. We also studied how the advent of the Feminist Initiative has affected the established parties' priorities and profiling the issues of gender equality and feminism. We also focus on earlier research on feminism.Parties look different on feminism and gender equality. After interviewing the desired parties it will show clear links with PSO-theory. We also studied whether the parties have put feminism and gender equality higher up on the political agenda since the Feminist Initiative's entry into politics.The original text is in Swedish.
19
SINGH, BHUPINDER. "A HYBRID MSVM COVID-19 IMAGE CLASSIFICATION ENHANCED USING PARTICLE SWARM OPTIMIZATION." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18864.
Full textAbstract:
COVID-19 (novel coronavirus disease) is a serious illness that has killed millions of
civilians and affected millions around the world. Mostly as result, numerous
technologies that enable both the rapid and accurate identification of COVID-19
illnesses will provide much assistance to healthcare practitioners. A machine learning-
based approach is used for the detection of COVID-19. In general, artificial intelligence
(AI) approaches have yielded positive outcomes in healthcare visual processing and
analysis. CXR is the digital image processing method that plays a vital role in the
analysis of Covid-19 disease. Due to the maximum accessibility of huge scale annotated
image databases, excessive success has been done using multiclass support vector
machines for image classification. Image classification is the main challenge to detect
medical diagnosis. The existing work used CNN with a transfer learning mechanism
that can give a solution by transferring information from GENETIC object recognition
tasks. The DeTrac method has been used to detect the disease in CXR images. DeTrac
method accuracy achieved 93.1~ 97 percent. In this proposed work, the hybridization
PSO+MSVM method has worked with irregularities in the CXR images database by
studying its group distances using a group or class mechanism. At the initial phase of
the process, a median filter is used for the noise reduction from the image. Edge
detection is an essential step in the process of COVID-19 detection. The canny edge
detector is implemented for the detection of edges in the chest x-ray images. The PCA
(Principal Component Analysis) method is implemented for the feature extraction
phase. There are multiple features extracted through PCA and the essential features are
optimized by an optimization technique known as swarm optimization is used for
feature optimization. For the detection of COVID-19 through CXR images, a hybrid
multi-class support vector machine technique is implemented. The PSO (particle swarm
optimization) technique is used for feature optimization. The comparative analysis of
various existing techniques is also depicted in this work. The proposed system has
achieved an accuracy of 97.51 percent, SP of 97.49 percent, and 98.0 percent of SN.
The proposed system is compared with existing systems and achieved better
performance and the compared systems are DeTrac, GoogleNet, and SqueezeNet.
20
Olekas, Patrick T. "Characterization and Heuristic Optimization of Complex Networks." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1224187184.
Full text
21
Cleghorn, Christopher Wesley. "A Generalized theoretical deterministic particle swarm model." Diss., University of Pretoria, 2013. http://hdl.handle.net/2263/33333.
Full textAbstract:
Particle swarm optimization (PSO) is a well known population-based search algorithm,
originally developed by Kennedy and Eberhart in 1995. The PSO has been utilized
in a variety of application domains, providing a wealth of empirical evidence for its
effectiveness as an optimizer. The PSO itself has undergone many alterations subsequent
to its inception, some of which are fundamental to the PSO's core behavior, others have
been more application specific. The fundamental alterations to the PSO have to a large
extent been a result of theoretical analysis of the PSO's particle's long term trajectory.
The most obvious example, is the need for velocity clamping in the original PSO. While
there were empirical fndings that suggested that each particle's velocity was increasing
at a rapid rate, it was only once a solid theoretical study was performed that the reason
for the velocity explosion was understood. There has been a large amount of theoretical
research done on the PSO, both for the deterministic model, and more recently for the
stochastic model.
This thesis presents an extension to the theoretical deterministic PSO model. Under the
extended model, conditions for particle convergence to a point are derived. At present
all theoretical PSO research is done under the stagnation assumption, in some form or
another. The analysis done under the stagnation assumption is one where the personal
best and neighborhood best are assumed to be non-changing. While analysis under the
stagnation assumption is very informative, it could never provide a complete description
of a PSO's behavior. Furthermore, the assumption implicitly removes the notion of
a social network structure from the analysis. The model used in this thesis greatly
weakens the stagnation assumption, by instead assuming that each particle's personal
best and neighborhood best can occupy an arbitrarily large number of unique positions.
Empirical results are presented to support the theoretical fndings.Dissertation (MSc)--University of Pretoria, 2013.
gm2014
Computer Science
Unrestricted
22
Mazin, Asim Mohamed. "REDUCING THE PEAK TO AVERAGE POWER RATIO OF MIMO-OFDM USING Particle SWARM OPTIMIZATION BASED PTS." OpenSIUC, 2013. https://opensiuc.lib.siu.edu/theses/1130.
Full textAbstract:
Asim M. Mazin, for the Master of Science degree in Electrical and Computer Engineering, presented on Mar 27, 2013, at Southern Illinois University Carbondale. TITLE: REDUCING THE PEAK TO AVERAGE POWER RATIO OF MIMO-OFDM USING PSO BASED PTS. MAJOR PROFESSOR: Dr. Garth V. Crosby, In this thesis we proposed PSO based PTS to accomplish the lowest Peak-to-Average Power Ratio of MIMO-OFDM system. We applied the PSO based PTS on each antenna of the system in order to find the optimal phase factors which is a straightforward method to get the minimum PAPR in such a system. The performance of PSO based PTS algorithm in MIMO-OFDM with a wide range of phase factor tends to give a high performance according to the simulation results. In addition, there is no need to increase the number of particles of the PSO algorithm to enhance the performance of the system, which keeps the complexity of finding the minimum PAPR reasonable.
23
REIS, Felipe Andery. "Procedimento de Ajuste de Parâmetros de Redes RBF via PSO." reponame:Repositório Institucional da UNIFEI, 2014. http://repositorio.unifei.edu.br:8080/xmlui/handle/123456789/292.
Full textAbstract:
Submitted by repositorio repositorio (repositorio@unifei.edu.br) on 2016-01-20T12:46:13Z
No. of bitstreams: 1
dissertacao_reis_2014.pdf: 1856078 bytes, checksum: 33fce3e106740e6125c35aa6b2e14fed (MD5)Made available in DSpace on 2016-01-20T12:46:13Z (GMT). No. of bitstreams: 1 dissertacao_reis_2014.pdf: 1856078 bytes, checksum: 33fce3e106740e6125c35aa6b2e14fed (MD5) Previous issue date: 2014-12
As redes neurais de funções de base radial (RBF - Radial Basis Function) têm sido utilizadas para a resolução de vários problemas em diversos contextos. Os parâmetros de uma rede de base radial (valores de centros, larguras e pesos) têm grande influência na sua capacidade de mapear relações entre seus dados de entrada e saída. Algumas abordagens apresentam procedimentos diversificados para determinar e otimizar estes parâmetros. Este trabalho aborda a combinação de métodos não supervisionados com o algoritmo de enxame de partículas (PSO - Particle Swarm Optimization) para a determinação de parâmetros em redes RBF. O algoritmo de otimização realiza um refinamento nos valores das larguras das funções de base radial a partir de um procedimento prévio de seleção de parâmetros. Utilizando valores pré-ajustados, o algoritmo converge em um menor número de passos em relação aos parâmetros inicializados aleatoriamente. O uso da abordagem proposta proporciona uma boa melhoria na exatidão de modelos de redes RBF em aplicações de aproximação de funções, previsão de série temporal e classificação de padrões.
24
Gobbo, Ilaria <1994>. "PSO per problemi di tracking error: selezione di piccoli portafogli." Master's Degree Thesis, Università Ca' Foscari Venezia, 2019. http://hdl.handle.net/10579/14220.
Full textAbstract:
In questo elaborato di selezione di portafoglio viene applicata la metaeuristica PSO per il problema di Tracking Error (TE). Nei primi capitoli viene analizzata la teoria di gestione e selezione di portafoglio: si distingue la gestione passiva dalla gestione attiva; viene introdotto il modello di Markowitz e ne vengono analizzati i punti di forza ed i limiti. Si approfondisce successivamente la Particle Swarm Optimization, e la si applica al problema di minimizzazione del TE. Viene selezionato ed analizzata la gestione di un portafoglio profilato per un piccolo investitore.
25
Mazzucato, Nicolo' <1992>. "PSO and BFO: two alternative metaheuristics for portfolio optimization problem." Master's Degree Thesis, Università Ca' Foscari Venezia, 2019. http://hdl.handle.net/10579/14538.
Full textAbstract:
As part of Modern Portfolio Theory, Portfolio management is a subject introduced by Markowitz back in 1950s. In Chapter 1 of this paper, we describe the Mean- Variance portfolio selection model proposed by Markowitz. Since its introduction, this model has been considered as the standard model. Although it has many advantages, with the years passing and the increased complexity of the markets, the model showed all its limits. The main drawbacks arises from the unrealistic assumptions at the base of the model. In other words, the assumptions are not able to present the real world and the risk measure used. Therefore, there was the need of a new class of risk measures, coherent risk measure, suitable for financial portfolios. The coherent measure of risk that belongs to this class chosen for this paper is the two-sided risk measure introduced by Chen and Wang in 2008.
Chapter 2 describes the metaheuristics and in particular focuses on those chosen for this paper. Metaheuristic can be describe as trial and error optimization techniques able to find high level solutions to complex problems. Those high-level solutions, although high quality solutions, are not the optimal ones. However, metaheuristics find good solutions in a reasonable amount of time. In this paper we decided to choose bio-inspired metaheuristics, in particular Particle Swarm Optimization and Bacterial Foraging Optimization.
In Chapter 3 we presented an alternative model to the one introduced by Markowitz, that is the realistic portfolio proposed by Corazza, Fasano and Gusso. This strategy allows to make the analysis more realistic by overcoming the limits of the model described in Chapter 2. However, in order to effectively solve the NP-hard problem that arises from the use of the two-sided risk measure combined with the realistic portfolio chosen, we applied an exact penalty method, which allows to transform the constrained problem into an unconstrained one.
Finally, in Chapter 4, we applied PSO and BFO to solve the portfolio selection problem presented in the previous chapter. For this application, the data used are the daily closing prices of DAX 30 index from March 2014 to November 2018. The periods considered are eight, and each one consists of 8 in-sample months and 3 out-of-sample. In addition, we also analyzed the respect of the monotonicity property of the risk measure. Lastly, we carried out a comparison between the given respectively between PSO and BFO.
26
Henniges, Philippe. "PSO pour l'apprentissage supervisé des réseaux neuronaux de type fuzzy ARTMAP." Mémoire, École de technologie supérieure, 2006. http://espace.etsmtl.ca/508/1/HENNIGES_Pihilippe.pdf.
Full textAbstract:
Dans ce mémoire, nous avons étudié les divers comportements d'un type de réseau de neurones en particulier, soit le réseau fuzzy ARTMAP (FAM), dans le but de développer une stratégie d'apprentissage spécialisée pour ce type de réseau. Pour ce faire, nous avons observé les effets de plusieurs caractéristiques sur ce type de réseau, soit: la taille de la base de données d'entraînement, les stratégies d'apprentissage standard, la technique de normalisation, la structure du chevauchement, la polarité du MatchTracking ainsi que l'influence des paramètres internes du réseau fuzzy ARTMAP. Ces effets sont mesurés au niveau de la qualité et des ressources utilisées par le réseau FAM à travers des bases de données synthétiques et réelles.
Nous avons remarqué que le réseau FAM présente une dégradation de performances due à un effet de sur-apprentissage créé par le nombre de patrons d'entraînement et le nombre d'époques d'apprentissage, et ce, avec les bases de données possédant un degré de chevauchement. Pour éviter ce problème, nous avons développé une stratégie d'apprentissage spécialisée pour les réseaux FAM. Celle-ci permet d'améliorer les performances en généralisation en utilisant l'optimisation par essaims particulaires ou PSO (anglais pour "Particle Swarm Optimization") pour optimiser la valeur des quatre paramètres internes FAM (α, β, є et ρ).
Cette stratégie spécialisée obtient lors de toutes nos simulations, tant avec les bases de données synthétiques que réelles, de meilleures performances en généralisation que lors de l'utilisation des stratégies d'apprentissage standard utilisant les paramètres standard des réseaux FAM (MT+, MT-). De plus, elle permet d'éliminer la majorité de l'erreur de sur-apprentissage due à la taille de la base d'entraînement et au nombre d'époques d'apprentissage. Ainsi, cette stratégie spécialisée pour FAM a démontré que la valeur des paramètres internes du réseau FAM a un impact considérable sur les performances du réseau. De plus, pour toutes les bases testées, les valeurs optimisées des paramètres sont généralement toutes éloignées de leurs valeurs standard (MT-et MT+), lesquelles sont majoritairement utilisées lors de l'emploi du réseau FAM.
Cependant, cette stratégie d'apprentissage spécialisée n'est pas consistante avec la philosophie « on-line » de la famille ART, car la valeur des paramètres n'est pas optimisée séquentiellement. Malgré tout, elle permet d'indiquer les zones de performances optimales pouvant être atteintes par le réseau fuzzy ARTMAP. À notre connaissance, c'est la première fois qu'une stratégie d'apprentissage pour FAM utilise l'optimisation des valeurs des quatre paramètres internes de ce réseau.
27
Franz, Wayne. "Multi-population PSO-GA hybrid techniques: integration, topologies, and parallel composition." Springer, 2013. http://hdl.handle.net/1993/23842.
Full textAbstract:
Recent work in metaheuristic algorithms has shown that solution quality may be improved by composing algorithms with orthogonal characteristics. In this thesis, I study multi-population particle swarm optimization (MPSO) and genetic algorithm (GA) hybrid strategies. I begin by investigating the behaviour of MPSO with crossover, mutation, swapping, and all three, and show that the latter is able to solve the most difficult benchmark functions. Because GAs converge slowly and MPSO provides a large degree of parallelism, I also develop several parallel hybrid algorithms. A composite approach executes PSO and GAs simultaneously in different swarms, and shows advantages when arranged in a star topology, particularly with a central GA. A static scheme executes in series, with a GA performing the exploration followed by MPSO for exploitation. Finally, the last approach dynamically alternates between algorithms. Hybrid algorithms are well-suited for parallelization, but exhibit tradeoffs between performance and solution quality.
28
Chi, Wen-Chun, and 紀玟君. "Parallel QBL-PSO Using MapReduce." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/42584912905270860447.
Full text
29
Franken, Cornelis J. "PSO-based coevolutionary Game Learning." Diss., 2004. http://hdl.handle.net/2263/30166.
Full textAbstract:
Games have been investigated as computationally complex problems since the inception of artificial intelligence in the 1950’s. Originally, search-based techniques were applied to create a competent (and sometimes even expert) game player. The search-based techniques, such as game trees, made use of human-defined knowledge to evaluate the current game state and recommend the best move to make next. Recent research has shown that neural networks can be evolved as game state evaluators, thereby removing the human intelligence factor completely. This study builds on the initial research that made use of evolutionary programming to evolve neural networks in the game learning domain. Particle Swarm Optimisation (PSO) is applied inside a coevolutionary training environment to evolve the weights of the neural network. The training technique is applied to both the zero sum and non-zero sum game domains, with specific application to Tic-Tac-Toe, Checkers and the Iterated Prisoners Dilemma (IPD). The influence of the various PSO parameters on playing performance are experimentally examined, and the overall performance of three different neighbourhood information sharing structures compared. A new coevolutionary scoring scheme and particle dispersement operator are defined, inspired by Formula One Grand Prix racing. Finally, the PSO is applied in three novel ways to evolve strategies for the IPD – the first application of its kind in the PSO field. The PSO-based coevolutionary learning technique described and examined in this study shows promise in evolving intelligent evaluators for the aforementioned games, and further study will be conducted to analyse its scalability to larger search spaces and games of varying complexity.Dissertation (MSc)--University of Pretoria, 2005.
Computer Science
unrestricted
30
Lai, Yi-fong, and 賴易烽. "PSO Algorithm for Speaker Verification." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/19867975602889928362.
Full textAbstract:
碩士國立中央大學
電機工程研究所
100
This thesis proposed method uses PSO algorithm to develop the VQ algorithm and determinate the parameter of SVM. Particle swarm optimization (PSO) simulates social behavior such as birds flocking to a promising position to achieve precise objectives in a multi-dimensional space. PSO performs searches using a population (called swarm) of individuals (called particles) that are updated from iteration to iteration. The vector quantization (VQ) was a powerful technique in the applications of digital speech compression. The traditionally widely used method such as the Linde-Buzo-Gray (LBG) algorithm always generated local optimal codebook. This thesis proposed method uses PSO algorithm to develop the VQ algorithm. Experimental results showed that the PSO algorithm can provide a better codebook with smaller mean square error (MSE) and less computation time than LBG algorithm. In the support vector machines (SVM), the model for classification is generated from the training process with the training data. Later on, classification is executed based on trained model. The largest problems encountered in setting up the SVM model are how to select the kernel function and its parameter values. This thesis proposed a method uses PSO algorithm to determinate the SVM parameter. Experimental results showed that the proposed system obtains a 2.26% EER and 0.0275 DCF improvement over the system with grid search.
31
Kao, Chih-Chieh, and 高志杰. "PSO Algorithm for Mel- Filterbank." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/80671820970339415326.
Full textAbstract:
碩士國立中央大學
電機工程學系
101
In this thesis, a study for feature extraction using filter bank applied to mel frequency cepstrum coefficients (MFCC) is presented. We propose a novel approach to use particle swarm optimization (PSO) to optimize the parameters of MFCC filterbank, such as the central and side frequencies. The proposed PSO algorithm utilizes filter similarity between statistical curve and filterbank’s envelope as fitness function. According to the energy and energy difference statistical charts that comply with characteristics of the speech signal in the energy spectrum, we obtained two optimal results by PSO. Then keyword recognization and three noisy environments are considered for tests. The results of our experiments show that the proposed method improves the recognition rate of keyword spotting system and the robustness against the testing noisy environments.
32
Liu, Te-chen, and 劉德誠. "A PSO Based Face Detection System." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/89072898476870568717.
Full textAbstract:
碩士長庚大學
資訊管理研究所
94
This paper proposes a Particle Swarm Optimization (PSO) based human face detection system. The system integrates PSO and neural networks. It can provide a more accurate face detection service by gaining faces’ position, size and angle. Engineers have interested in the face detection technologies for a very long time. At present, there are many applied face detection methodologies. The machine learning methods are one kind of these methodologies that need fewer man-made definitions. Only if there are enough training data collections, machine learning technologies can perform as a reliable face detector. And, the neural network classifier is one of these successful cases. In the pass, most of the pure machine learning based face detection system could only detect faces’ position, but not included their size and angle. This system integrates PSO and neural networks, and it can keep the advantage of the machine learning and moreover aware the faces’ size and angle. Besides proposing the framework of the PSO face detection system, this research also provide two PSO parameter setting formula. The Variable Confidence (VC) and Fast Variable Confidence (FVC) are two simple formulas that can promote the performance of the PSO face detection. These two methods improve both the detect rate and error rate substantially. We used the “Taiwanese Facial Dataset” and JAFFE image database as the experiment data, and tested tree PSO detection methods and different population size. The result shows that the VCPSO can meet the detect rate at 97.5%. The FVCPSO can save almost 50% of the iteration time, but only have accuracy down about 5%. These results point out that the detection methods we proposed are practical for the reality world. They provide a feasible way for the applications which need the detail of the faces’ information.
33
Lin, Shu-Yu, and 林書宇. "Improving PSO by Query-Based Learning." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/34kyp3.
Full textAbstract:
碩士國立臺灣大學
工程科學及海洋工程學研究所
95
Motivation: PSO (particle swarm optimization) is one of the most important research topics on artificial intelligence. PSO still remain some disadvantages. This paper tries to discuss the disadvantages of PSO and to find a solution for improving its performance. Method: We apply the query-based learning method proposed in our previous papers to PSO. It leads the particles to extend their search area. Thus, not only the precision of solution but also the time consumed is improved. We visualize the mechanism through a two-dimension PSO and verify the mechanism by several functions. Conventional PSO usually leads the particles go into the wrong direction of evolution. To resolve this drawback, when particles tend to converge, we spread some particles into ambiguous solution space. Furthermore, PSO has been well improved. Achievement: This thesis, in our knowledge, is the first study that applies the QBL concept in Particle Swarm Optimization. The experiment results show the proposed approach is able to prevent the system from falling into local optimal and improve the performance of PSO.
34
Chen, WeiRen, and 陳韋任. "PSO-based Fuzzy Image Filter Design." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/29702015981924462606.
Full textAbstract:
碩士國立宜蘭大學
電子工程學系碩士班
99
In this paper, we employ a fuzzy image filter (FIF) to reducing noise for gray-level image and the parameters of the FIF are adjusted by using the Particle Swarm Optimization (PSO) technique. It is well known that PSO has fast convergence speed to find the global optimal parameters of FIF and thus it will have better performance for removing the impulse noise even from highly corrupted images. The filter consists of a fuzzy number construction process, a fuzzy filtering process, a PSO searching process and an image knowledge base. First, we construct the image knowledge base for the fuzzy filtering process as that in [30]. Then, we can collect data when training noised images pass through the fuzzy filtering process and adjust each of the parameters to global optimum value by PSO. Thus, the proposed FIF can be constructed more efficient and powerful. In order to show the ability of this FIF, we compare with some denoising methods in the experimental results. At last, applying the same framework again, we will utilize a metric Q who was proposed by Zhu[29] and when it was without knowledge of the noise-free image and provides a quantitative measure of true image content from the noise image, to reduction Impulse noise.
35
Li, zeyou, and 李則佑. "Applying PSO-SVM For Channel Equalization." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/21964865425522112791.
Full textAbstract:
碩士國立宜蘭大學
電機工程學系碩士班
100
The support vector machine (SVM) is a powerful tool for solving problems with high dimensional, nonlinearly, and is of excellent performance in classification. In this study, we propose SVM as channel equalization. To reconstruct the signal that has the inter symbol interference (ISI) and white Gaussian noise which in high speed communications environments. The SVM parameters will affect the identification of the result. Therefore, we use particle swarm optimization (PSO) to find the suit parameters in SVM. To obtain the channel equalization model and reconstruct the signal. The PSO-SVM equalizer to realize the Bayesian equalizer solution can be achieved efficiently. The performance degradation was nearly 1dB at SNR increased.
36
Su, Hua, and 蘇樺. "PSO Algorithm for Speaker Verification Systems." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/64080634241659055321.
Full textAbstract:
碩士國立中央大學
電機工程學系
102
This thesis focused on speaker verification between test corpus and registered speaker models. First of all, the thesis introduces score normalization approaches to the speaker verification system. Then, we apply Particle Swarm Optimization algorithm to optimize model parameters. The main idea of PSO method is like fish foraging behavior. All particles of PSO have memories. The algorithm has simple calculation and fast convergence. With its optimized features to build a more accurate speaker model, the system is more discernment. In addition, the thesis also introduces a regression analysis method to speaker verification system. Regression analysis is a useful statistics analysis method. We build the regression model for each speaker by ordinary least squares estimation and the coefficients of determination analysis. Experiments showed that the proposed method can improve performance of the speaker verification system.
37
Tang, Chung-Hao, and 唐中浩. "Applying Adaptive PSO on Roundness Measurement." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/8eqku6.
Full textAbstract:
碩士國立臺北科技大學
工業工程與管理研究所
97
Inspection on silicon wafers is a complex and important process for semiconductor manufacturers. Optimally manufacturing each wafer to overcome the quartz shortages is tantamount to achieve maximum total profit in practice. Roundness, particularly the roundness of silicon wafers remaining a bottleneck for reclaiming wafer, is a very costly and crucial step for increasing yield. In particular, inspecting post-slicing process of wafers can be considered as a non-linear problem with a specified roundness measure. Therefore, this study proposes heuristic and adaptive methods that rapidly converge with high accuracy and low cost. The proposed methods incorporate the Hooke-Jeeves pattern search with Particle Swarm Optimization in comparison of convergent performance. A substantial amount of effort has been expended to alleviate the redundancy than the former [18] involved. This study primarily focuses on mixture algorithms for measuring roundness of silicon wafers and competes the performance with accuracy (efficiency) through visual inspection. A set of experiments is conducted to verify the feasibility under varied schemes. Definitively, experimental results reveal that the proposed method is superior in terms of execution time and solution quality.
38
Prasain, Hari. "A Parallel Particle Swarm Optimization Algorithm for Option Pricing." 2010. http://hdl.handle.net/1993/4033.
Full textAbstract:
Financial derivatives play significant role in an investor's success.
Financial option is one form of derivatives.
Option pricing is one of the challenging and fundamental
problems of computational finance. Due to highly volatile and dynamic market
conditions, there are no closed form solutions available except
for simple styles of options such as, European options.
Due to the complex nature of the governing mathematics, several
numerical approaches have been proposed in the past to price American style and
other complex options approximately.
Bio-inspired and nature-inspired algorithms have been considered for
solving large, dynamic and complex scientific and engineering problems.
These algorithms are inspired by techniques developed by the insect
societies for their own survival. Nature-inspired algorithms, in particular,
have gained prominence in real world optimization problems such as in mobile ad hoc
networks. The option pricing problem fits very well into this category
of problems due to the ad hoc nature of the market. Particle swarm
optimization (PSO) is one of the novel global search algorithms based
on a class of nature-inspired techniques known as swarm intelligence.
In this research, we have designed a sequential PSO based option pricing algorithm
using basic principles of PSO. The algorithm is applicable for both
European and American options, and handles both constant and variable volatility.
We show that our results for European options compare well with
Black-Scholes-Merton formula.
Since it is very important and critical to lock-in profit making opportunities in
the real market, we have also designed and developed parallel algorithm to expedite
the computing process.
We evaluate the performance of our algorithm on a cluster of
multicore machines that supports three different architectures: shared memory,
distributed memory, and a hybrid architectures.
We conclude that for a shared memory
architecture or a hybrid architecture, one-to-one mapping
of particles to processors is recommended for performance
speedup. We get a speedup of 20 on a cluster of four nodes
with 8 dual-core processors per node.
39
Sidhu, Manitpal S. "A PSO based load-rebalance algorithm for task-matching in large scale heterogeneous computing systems." 2013. http://hdl.handle.net/1993/21692.
Full textAbstract:
The idea of utilizing nature inspired algorithms to find near optimal solutions to various real world NP complete optimization problems has been extensively explored by researchers. One such problem is the task matching problem in large heterogeneous distributed computing environments like Grids and Clouds. Researchers have explored Particle Swarm Optimization(PSO), which is branch of swarm intelligence, to find a near optimal solution for the task matching problem.
In this work, I investigated the effectiveness of the smallest position value (SPV) technique in mapping the continuous version of the PSO algorithm to the task matching problem in a heterogeneous computing environment. The experimental evaluation demonstrated that the task matching generated by this technique will result in an imbalanced load distribution. In this work, I have therefore also designed a load-rebalance PSO heuristic (PSO-LR) that results in minimization of makespan and balanced utilization of the available compute nodes even in heterogeneous computing environments.
40
Emma and 蔣雅慈. "Multi-Objective Nurse Scheduling Using Scatter PSO." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/64296188743774598841.
Full textAbstract:
碩士國立暨南國際大學
資訊管理學系
97
It is time-consuming to generate nurse scheduling using traditional human-involved manner in order to account for administrative operations, business benefits, governmental regulations, and fairness perceived by nurses. Moreover, the objectives cannot be measured quantitatively even when the nurse scheduling is generated after a lengthy manual process. This paper presents a Multi-Objective Scatter PSO combined with Tabu Search to tackle the real-world nurse scheduling problem. By the proposed mathematical formulation, the hospital administrator can set up multiple objectives (such as cost reduction and nurse-satisfaction raising) and stipulate a set of scheduling constraints (such as operational practice and governmental regulations), and our system can automatically generate a set of solutions which nearly optimize the given objectives and meet the specified constraints. We used two kinds of problems to evaluate the performance of Scatter MOPSO, first is benchmark functions and second is nurse scheduling problem. The experimental results manifest that our method performs better than NSGA II and MOPSO on benchmark functions, and better than MOPSO on nurse scheduling problem.
41
Wu, Cheng-Pei, and 吳政沛. "PSO-based Localization in Wireless Sensor Networks." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/09485581456766144695.
Full textAbstract:
碩士淡江大學
電機工程學系碩士班
96
Localization in wireless sensor networks has developed two categories: range-free and range-based localization algorithms. The range-free algorithms don''t need any range techniques but use connectivity among the anchors to estimate the positions of unknown nodes. The range-based algorithms must need some range techniques such as TOA, TDOA, AOA and RSSI to measure the neighbors'' distance. And use these measurements to estimate the position of the unknown nodes. In order to add the coverage of anchor nodes, some range-based localization algorithms use iterative multilateration to solve low density problem of the anchor nodes. But the iterative multilateration algorithm suffers two drawbacks: first, some nodes still don''t have sufficient anchor nodes in their neighborhood; second, the use of localized unknown nodes as anchor nodes can bring the cumulative error. Therefore, we propose a PSO based localization algorithm using the distance of the closest neighbor to estimate the unknown node''s location. We use this algorithm to reduce the error accumulation effects and add the probability of the orphan nodes which can successfully calculate the locations. Compared with some localization algorithms, new method can be more effective performance for different environment in our computer simulations.
42
Wu, Tsan-Chan, and 吳讚展. "Self-adjusted Nonlinear inertia weight PSO algorithm." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/26009556464934248971.
Full textAbstract:
碩士國立中央大學
電機工程研究所
100
In this thesis we have presented an improved algorithm for Particle Swarm Optimization (PSO) named Self-adjusted Nonlinear inertia weight PSO algorithm (SNPSO). SNPSO algorithm is an improved method of the inertia weight, utilize nonlinear and self-modulation characteristics to improve PSO algorithm that is easy to trap into the local optimal solution, The thesis also presents a method of searching parameters in the SNPSO. Finally, The performance of SNPSO is fairly demonstrated by applying sixteen benchmark problems and comparing it with several popular PSO algorithm. The analysis of result shows that our proposed methods is effective and gain better performance than other popular PSO algorithms. Furthermore, our method can efficiently improve the performance of standard PSO and more ability to prevent the particle fall into some local optimal solutions.
43
Yan, Yu-shiang, and 顏淯翔. "Visual Tracking System Based on Improved PSO." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/46901353322747813785.
Full textAbstract:
碩士國立中央大學
電機工程學系
102
In this thesis, we propose a modified particle swarm optimization algorithm which is called particle swarm optimization with one dimension multi-modes (ODMPSO). The proposed ODMPSO which is different from standard PSO algorithm is moving functions. In ODMPSO method, the particles can be adaptively searched by their environment. There are five modes in ODMPSO method. Each mode has its own specific optimizations. Finally, these modes makes the particles more easily and quickly find the results. Afterwards, we propose a Gaussian mixture model based on ODMPSO (GMM-ODMPSO) method in a visual tracking system. The GMM-ODMPSO method will accelerate the convergence rate of creating the GMM background model and the system also improves the detection of moving targets. The experimental results show that the proposed GMM background model obtains better recognition rate. As seen in the experiments, the GMM-ODMPSO method is a 48% improvement over the computing time, 88% over the convergence rate, and the recognition rate is almost the same as the traditional GMM background model. In the results, we can see our proposed method is more effective.
44
KUMAR, ANURAG. "ECONOMIC LOAD DISPATCH STUDIES BASED ON PSO." Thesis, 2020. http://dspace.dtu.ac.in:8080/jspui/handle/repository/20135.
Full textAbstract:
In this project, an Improved PSO algorithm has been developed to solve economic load
dispatch problem. In the Proposed PSO algorithm, Retardation factor has been introduced to
damp out the oscillations as the particle reaches near the global optimum point. This results in
faster convergence as well as lesser cost of generation. The proposed algorithm has been
implemented on unconstrained mathematical test functions to check the accuracy and
convergence of the algorithm. The Proposed PSO algorithm is implemented on IEEE three and
six generator thermal power plants. In the case of mathematical test functions, the comparison
is done in terms of the number of iterations performed and the number of function evaluations.
In case of an economic load dispatch problem, the comparison is done in terms of fuel cost of
generation also. After comparing results in both cases, it is found that the proposed PSO
algorithm gives more accurate results in less number of iterations. Number of iterations,
number of function evaluations, and time consumed have been measured for different values
of retardation factors. Best retardation is the one for which function gets optimized in minimum
number of iterations. MATLAB simulation is done to solve the economic load dispatch
problem and mathematical test function using Proposed algorithm and Basic particle swarm
optimization algorithm.
45
VARSHNEY, PRATEEK KUMAR VARSHNEY. "IMPLEMENTING PARALLEL PSO ALGORITHM USING MAPREDUCE ARCHITECTURE." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/14678.
Full textAbstract:
ABSTRACT
Optimization is the problem of finding minimum or maximum of a given objective function relative to some set, often representing a range of choices available in a certain situation. Particle Swarm Optimization (PSO) is a simple and effective evolutionary algorithm, but it may take a reasonable time to optimize complex objective functions which are deceptive or expensive. To avoid being trapped in local optima, Particle Swarm Optimization requires extensive exploration for multimodal and multidimensional functions. Expensive functions whose computational complexity may arise from dependence on detailed simulations or large datasets, takes a long time to evaluate. For such functions PSO must be parallelized to use multiprocessor systems and clusters efficiently. Parallelization of PSO can lead to scalable speedup in performance. PSO can be naturally expressed in Google’s MapReduce framework to develop a simple and robust parallel implementation.
To improve optimization of difficult objective functions and to improve parallel performance, modifications could be made to this flexible implementation of the algorithm. In the proposed work the classic Particle Swarm Optimization Algorithm has been implemented on Big Data platform Hadoop using MapReduce Architecture. The algorithm has been applied to optimize parameters of basic COCOMO Model need to calculate effort of the project.
The experiments show that the Hadoop could carry out big data calculations which normal serial PSO could not. The proposed model would have better efficiency for intensive computational functions.
46
Mishra, Mahesh Prasad. "Solution to economic load dispatch using PSO." Thesis, 2012. http://ethesis.nitrkl.ac.in/3711/1/FINALTHESIS.pdf.
Full textAbstract:
The modern power system around the world has grown in complexity of interconnection and power demand. The focus has shifted towards enhanced performance, increased customer focus, low cost, reliable and clean power. In this changed perspective, scarcity of energy resources, increasing power generation cost, environmental concern necessitates optimal economic dispatch. In reality power stations neither are at equal distances from load nor have similar fuel cost functions. Hence for providing cheaper power, load has to be distributed among various power stations in a way which results in lowest cost for generation. Practical economic dispatch (ED) problems have highly non-linear objective function with rigid equality and inequality constraints. Particle swarm optimization (PSO) is applied to allot the active power among the generating stations satisfying the system constraints and minimizing the cost of power generated. The viability of the method is analyzed for its accuracy and rate of convergence. The economic load dispatch problem is solved for three and six unit system using PSO and conventional method for both cases of neglecting and including transmission losses. The results of PSO method were compared with conventional method and were found to be superior. The conventional optimization methods are unable to solve such problems due to local optimum solution convergence. Particle Swarm Optimization (PSO) since its initiation in the last 15 years has been a potential solution to the practical constrained economic load dispatch (ELD) problem. The optimization technique is constantly evolving to provide better and faster results.
47
pal, Sushil, and Annwesh Barik. "PSO Based Deployment of Hybrid Sensor Networks." Thesis, 2015. http://ethesis.nitrkl.ac.in/6904/1/PSO_Pal_2015.pdf.
Full textAbstract:
With the rapid increase in the usage of wireless sensor networks, it is emerging as a technology for monitoring various physical activities. The essential characteristics of wireless sensor network are coverage, cost, connectivity and lifetime which are dependent upon the number and type of sensors being used for the required task. A random deployment strategy of sensor nodes may cause coverage holes in the sensing ?eld. The work presented here shall mainly focus on deployment strategy of WSNs which will improve the coverage area that poses the biggest challenge to the developers. Most of the problems related to WSNs are modelled and approached as multi objective functions through various genetic algorithms. PSO is one such technique that is e?cient and computationally e?cient in addressing various issues such as optimising sensor deployment and localization of sensor nodes. A modi?ed particle swarm optimization (PSO) technique using grid based strategy has been proposed for sensor deployment which is capable of e?ciently deploying the sensors with an objective of maximizing the coverage ratio. It will determine the optimum location of the mobile nodes after the initial random deployment .The optimality rate of this approach is also higher as compared to other genetic algorithms.
48
Ching-Yi, Chen. "PSO-Based Evolutionary Learning System Design and Applications /." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0002-2505200615111100.
Full text
49
Wu, Shang-Tza, and 吳尚澤. "A Dynamic PSO ─ Black-Scholes Option Pricing Model." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/76766122738842239555.
Full textAbstract:
碩士國立屏東商業技術學院
資訊管理系(所)
102
In the financial market, to analyze the trends of price change is capable to predominate the investment opportunities. Therefore, many scholars and researchers focused on the pricing theories of financial commodity, for example, the Black-Scholes option pricing model, but its arguments are predefined by some strongly assumptions. Because of those assumptions, it will be still difficult to evaluate the prices to real markets. Therefore, this research would give 4 different conditions and their impacts on option pricing to construct a hybrid-dynamic option pricing model (PSO-BS Option Pricing Model). By revising the controversial normal distribution assumption of traditional pricing model, the study has formed some new look up tables by several investment conditions. Without increasing pricing complexity of commodities, the result of the pricing model is obviously superior to traditional Black-Scholes option pricing model.
50
Wu, Chih-ping, and 吳致平. "PSO-based resource allocation in downlink LTE networks." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/77029494192799872760.
Full textAbstract:
碩士國立中央大學
通訊工程學系在職專班
102
In this thesis, the problem of resource allocation in downlink long term evolution (LTE) networks is investigated. To increase the spectral and power efficiency, we propose a subcarrier allocation scheme based on particle swarm optimization (PSO) algorithm. PSO can be easily implemented in discrete optimization problem and fast converge to an optimal solution, but the solution may be just a local optimum. In order to avoid trapping at local optima, the strategies of crossover and mutation are used in the proposed method. Simulation results show that the proposed algorithm can efficiently reduce the total transmission power.