19 research outputs found
On Some Aspects of Genetic and Evolutionary Methods for Optimization Purposes
In this paper, the idea of applying Baldwin effect in a hybrid genetic algorithm with gradient local search is formulated. For two different test functions is examined proposed version of algorithm. Research results are presented and discussed to show potential efficiency of applied Baldwin effect
Developing Load Balancing for IoT - Cloud Computing Based on Advanced Firefly and Weighted Round Robin Algorithms
أدى التطور في إنترنت الأشياء (IoT) إلى ربط البلايين من الأجهزة المادية غير المتجانسة معاً لتحسين نوعية الحياة البشرية، من خلال جمع البيانات من بيئتهم. يجب تخزين هذه البيانات الهائلة التي تم تجميعها في سعة تخزين كبيرة بالإضافة إلى قدرات حاسوبية عالية، التي توفيرها الحوسبة السحابية. يتم نقل بيانات أجهزة IoT باستخدام نوعين من البروتوكولات. نقل الرسائل في قائمة انتظار النقل (MQTT) وHypertext Transfer Protocol (HTTP). يهدف هذا البحث لتحسين أداء النظام وزيادة الموثوقية من خلال الاستخدام الفعال للموارد. من خلال، استخدام موازنة التحميل في الحوسبة السحابية لتوزيع عبء العمل ديناميكيًا عبر العقد لتجنب زيادة التحميل على أي مورد فردي. من خلال الجمع بين نوعين من الخوارزميات: الديناميكية خوارزمية (اليراعة المتقدمة (Advanced Firefly Algorithm والخوارزمية الثابتة (Weighted Round Robin Algorithm). وأظهرت النتيجة تحسن في استخدام الموارد وزيادة الإنتاجية وتقليل وقت وقت الاستجابة.The evolution of the Internet of things (IoT) led to connect billions of heterogeneous physical devices together to improve the quality of human life by collecting data from their environment. However, there is a need to store huge data in big storage and high computational capabilities. Cloud computing can be used to store big data. The data of IoT devices is transferred using two types of protocols: Message Queuing Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP). This paper aims to make a high performance and more reliable system through efficient use of resources. Thus, load balancing in cloud computing is used to dynamically distribute the workload across nodes to avoid overloading any individual resource, by combining two types of algorithms: dynamic algorithm (adaptive firefly) and static algorithm (weighted round robin). The results show improvement in resource utilization, increased productivity, and reduced response time
Garantia de privacidade na exploração de bases de dados distribuídas
Anonymisation is currently one of the biggest challenges when sharing sensitive
personal information. Its importance depends largely on the application
domain, but when dealing with health information, this becomes a more serious
issue. A simpler approach to avoid this disclosure is to ensure that all
data that can be associated directly with an individual is removed from the
original dataset. However, some studies have shown that simple anonymisation
procedures can sometimes be reverted using specific patients’ characteristics,
namely when the anonymisation is based on hidden key attributes.
In this work, we propose a secure architecture to share information from distributed
databases without compromising the subjects’ privacy. The work
was initially focused on identifying techniques to link information between
multiple data sources, in order to revert the anonymization procedures. In
a second phase, we developed the methodology to perform queries over
distributed databases was proposed. The architecture was validated using
a standard data schema that is widely adopted in observational research
studies.A garantia da anonimização de dados é atualmente um dos maiores desafios
quando existe a necessidade de partilhar informações pessoais de carácter
sensível. Apesar de ser um problema transversal a muitos domínios de
aplicação, este torna-se mais crítico quando a anonimização envolve dados
clinicos. Nestes casos, a abordagem mais comum para evitar a divulgação
de dados, que possam ser associados diretamente a um indivíduo, consiste
na remoção de atributos identificadores. No entanto, segundo a literatura,
esta abordagem não oferece uma garantia total de anonimato, que pode ser
quebrada através de ataques específicos que permitem a reidentificação dos
sujeitos.
Neste trabalho, é proposta uma arquitetura que permite partilhar dados
armazenados em repositórios distribuídos, de forma segura e sem comprometer
a privacidade. Numa primeira fase deste trabalho, foi feita uma análise
de técnicas que permitam reverter os procedimentos de anonimização. Na
fase seguinte, foi proposta uma metodologia que permite realizar pesquisas
em bases de dados distribuídas, sem que o anonimato seja quebrado. Esta
arquitetura foi validada sobre um esquema de base de dados relacional que
é amplamente utilizado em estudos clínicos observacionais.Mestrado em Ciberseguranç
Adaptive bio-inspired firefly and invasive weed algorithms for global optimisation with application to engineering problems
The focus of the research is to investigate and develop enhanced version of swarm intelligence firefly algorithm and ecology-based invasive weed algorithm to solve global optimisation problems and apply to practical engineering problems. The work presents two adaptive variants of firefly algorithm by introducing spread factor mechanism that exploits the fitness intensity during the search process. The spread factor mechanism is proposed to enhance the adaptive parameter terms of the firefly algorithm. The adaptive algorithms are formulated to avoid premature convergence and better optimum solution value. Two new adaptive variants of invasive weed algorithm are also developed seed spread factor mechanism introduced in the dispersal process of the algorithm. The working principles and structure of the adaptive firefly and invasive weed algorithms are described and discussed. Hybrid invasive weed-firefly algorithm and hybrid invasive weed-firefly algorithm with spread factor mechanism are also proposed. The new hybridization algorithms are developed by retaining their individual advantages to help overcome the shortcomings of the original algorithms. The performances of the proposed algorithms are investigated and assessed in single-objective, constrained and multi-objective optimisation problems. Well known benchmark functions as well as current CEC 2006 and CEC 2014 test functions are used in this research. A selection of performance measurement tools is also used to evaluate performances of the algorithms. The algorithms are further tested with practical engineering design problems and in modelling and control of dynamic systems. The systems considered comprise a twin rotor system, a single-link flexible manipulator system and assistive exoskeletons for upper and lower extremities. The performance results are evaluated in comparison to the original firefly and invasive weed algorithms. It is demonstrated that the proposed approaches are superior over the individual algorithms in terms of efficiency, convergence speed and quality of the optimal solution achieved
Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter
In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF
Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm
Abstract— Online transportation has become a basic
requirement of the general public in support of all activities to go
to work, school or vacation to the sights. Public transportation
services compete to provide the best service so that consumers
feel comfortable using the services offered, so that all activities
are noticed, one of them is the search for the shortest route in
picking the buyer or delivering to the destination. Node
Combination method can minimize memory usage and this
methode is more optimal when compared to A* and Ant Colony
in the shortest route search like Dijkstra algorithm, but can’t
store the history node that has been passed. Therefore, using
node combination algorithm is very good in searching the
shortest distance is not the shortest route. This paper is
structured to modify the node combination algorithm to solve the
problem of finding the shortest route at the dynamic location
obtained from the transport fleet by displaying the nodes that
have the shortest distance and will be implemented in the
geographic information system in the form of map to facilitate
the use of the system.
Keywords— Shortest Path, Algorithm Dijkstra, Node
Combination, Dynamic Location (key words