AIS Algorithm for Smart Antenna Application in WLAN

Increasing numbers of wireless local area networks (WLAN) replacing wired networks have an impact on wireless network systems, causing issues such as interference. The smart antenna system is a method to overcome interference issues in WLANs. This paper proposes an artificial immune system (AIS) for a switch beam smart antenna system. A directional antenna is introduced to aim the beam at the desired user. The antenna consists of 8 directional antennas, each of which covers 45 degrees, thus creating an omnidirectional configuration of which the beams cover 360 degrees. To control the beam switching, an inexpensive PIC 16F877 microchip was used. An AIS algorithm was implemented in the microcontroller, which uses the received radio signal strength of the mobile device as reference. This is compared for each of the eight beams, after which the AIS algorithm selects the strongest signal received by the system and the microcontroller will then lock to the desired beam. In the experiment a frequency of 2.4 GHz (ISM band) was used for transmitting and receiving. A test of the system was conducted in an outdoor environment. The results show that the switch beam smart antenna worked fine based on locating the mobile device

Artificial Immune Systems: Principle, Algorithms and Applications

The present thesis aims to make an in-depth study of adaptive identification, digital channel equalization, functional link artificial neural network (FLANN) and Artificial Immune Systems (AIS).Two learning algorithms CPSO and IPSO are also developed in this thesis. These new algorithms are employed to train the weights of a low complexity FLANN structure by way of minimizing the squared error cost function of the hybrid model. These new models are applied for adaptive identification of complex nonlinear dynamic plants and equalization of nonlinear digital channel. Investigation has been made for identification of complex Hammerstein models. To validate the performance of these new models simulation study is carried out using benchmark complex plants and nonlinear channels. The results of simulation are compared with those obtained with FLANN-GA, FLANN-PSO and MLP-BP based hybrid approaches. Improved identification and equalization performance of the proposed method have been observed in all cases

Sistema Imunológico Artificial com Parâmetros Fuzzy

Os Artificial Immune Systems (Sistemas Imunológicos Artificiais - SIA) sãoconstituídos a partir de técnicas inspiradas na teoria do sistema imunológico biológico.O princípio da seleção clonal garante a adaptação do organismo para combaterantígenos invasores através de uma resposta imunológica ativada pelo reconhecimentoentre o antígeno e o anticorpo. Esta resposta pode ser ativada mesmo com umreconhecimento aproximado, indicando que um Sistema Fuzzy pode ser utilizado paracontrolar este processo. Neste artigo é apresentado um modelo híbrido baseado em SIAe Sistemas Fuzzy, seguido da implementação e testes de um algoritmo aplicado àotimização multimodal e outro ao reconhecimento de padrões. Os resultados obtidosdemonstram que o modelo proposto pode ser aplicado ao reconhecimento de padrões deforma satisfatória, entretanto, não se mostrou adequado para a otimização multimodal

When hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms

We present a time complexity analysis of the Opt-IA artificial immune system (AIS). We first highlight the power and limitations of its distinguishing operators (i.e., hypermutations with mutation potential and ageing) by analysing them in isolation. Recent work has shown that ageing combined with local mutations can help escape local optima on a dynamic optimisation benchmark function. We generalise this result by rigorously proving that, compared to evolutionary algorithms (EAs), ageing leads to impressive speed-ups on the standard Image 1 benchmark function both when using local and global mutations. Unless the stop at first constructive mutation (FCM) mechanism is applied, we show that hypermutations require exponential expected runtime to optimise any function with a polynomial number of optima. If instead FCM is used, the expected runtime is at most a linear factor larger than the upper bound achieved for any random local search algorithm using the artificial fitness levels method. Nevertheless, we prove that algorithms using hypermutations can be considerably faster than EAs at escaping local optima. An analysis of the complete Opt-IA reveals that it is efficient on the previously considered functions and highlights problems where the use of the full algorithm is crucial. We complete the picture by presenting a class of functions for which Opt-IA fails with overwhelming probability while standard EAs are efficient

UAV Swarm Mission Planning in Dynamic Environment Using Consensus-Based Bundle Algorithm.

To solve the real-time complex mission-planning problem for Multiple heterogeneous Unmanned Aerial Vehicles (UAVs) in the dynamic environments, this paper addresses a new approach by effectively adapting the Consensus-Based Bundle Algorithms (CBBA) under the constraints of task timing, limited UAV resources, diverse types of tasks, dynamic addition of tasks, and real-time requirements. We introduce the dynamic task generation mechanism, which satisfied the task timing constraints. The tasks that require the cooperation of multiple UAVs are simplified into multiple sub-tasks to perform by a single UAV independently. We also introduce the asynchronous task allocation mechanism. This mechanism reduces the computational complexity of the algorithm and the communication time between UAVs. The partial task redistribution mechanism has been adopted for achieving the dynamic task allocation. The real-time performance of the algorithm is assured on the premise of optimal results. The feasibility and real-time performance of the algorithm are validated by conducting dynamic simulation experiments

Optimization of robotic assembly of printed circuit board by using evolutionary algorithm

This research work describes the development and evaluation of a custom application exploring the use of Artificial Immune System algorithms (AIS) to solve a component placement sequencing problem for printed circuit board (PCB) assembly. In the assembly of PCB’s, the component placement process is often the bottleneck and the equipment to complete component placement is often the largest capital investment

A multimodal and multiobjective approach for phylogenetic trees reconstruction

Orientador: Fernando Jose Von ZubenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo : A reconstrução de árvores filogenéticas pode ser interpretada como um processo sistemático de proposição de uma descrição arbórea para as diferenças relativas que se observam em conjuntos de atributos genéticos homólogos de espécies sob comparação. A árvore filogenética resultante apresenta uma certa topologia, ou padrão de ancestralidade, e os comprimentos dos ramos desta árvore são indicativos do número de mudanças evolutivas desde a divergência do ancestral comum. Tanto a topologia quanto os comprimentos de ramos são hipóteses descritivas de eventos não-observáveis e condicionais, razão pela qual tendem a existir diversas hipóteses de alta qualidade para a reconstrução, assim como múltiplos critérios de desempenho. Esta tese (i) aborda árvores sem raiz; (ii) enfatiza os critérios de quadrados mínimos, evolução mínima e máxima verossimilhança; (iii) propõe uma extensão ao algoritmo Neighbor Joining que oferece múltiplas hipóteses de alta qualidade para a reconstrução; e (iv) descreve e utiliza uma nova ferramenta para otimização multiobjetivo no contexto de reconstrução filogenética. São considerados dados artificiais e dados reais na apresentação de resultados, os quais apontam vantagens e aspectos diferenciais das metodologias propostasAbstract: The reconstruction of phylogenetic trees can be interpreted as a systematic process of proposing an arborean description to the relative dissimilarities observed among sets of homologous genetic attributes of species being compared. The resulting phylogenetic tree presents a certain topology, or ancestrality pattern, and the length of the edges of the tree will indicate the number of evolutionary changes since the divergence from the common ancestor. Both topology and edge lengths are descriptive hypotheses of non-observable and conditional events, which implies the existence of diverse high-quality hypotheses for the reconstruction, as long as multiple performance criteria. This thesis (i) deals with unrooted trees; (ii) emphasizes the least squares, minimum evolution, and maximum likelihood criteria; (iii) proposes an extension to the Neighbor Joining algorithm which offers multiple high-quality reconstruction hypotheses; and (iv) describes and uses a new tool for multiobjective optimization in the context of phylogenetic reconstruction. Artificial and real datasets are considered in the presentation of results, which points to some advantages and distinctive aspects of the proposed methodologiesDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric

Synergy between artificial immune systems and probabilistic graphical models

Orientador: Fernando Jose Von ZubenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Sistemas imunológicos artificiais (SIAs) e modelos gráficos probabilísticos são duas importantes técnicas para a construção de sistemas inteligentes e tem sido amplamente exploradas por pesquisadores das mais diversas áreas, tanto no aspecto teórico quanto pratico. Entretanto, geralmente o potencial de cada técnica é explorado isoladamente, sem levar em consideração a possível cooperação entre elas. Como uma primeira contribuição deste trabalho, é proposta uma metodologia que explora as principais vantagens dos SIAs como ferramentas de otimização voltadas para aprendizado de redes bayesianas a partir de conjuntos de dados. Por outro lado, os SIAs já propostos para otimização em espaços discretos e contínuos correspondem a meta-heurísticas populacionais sem mecanismos para lidarem eficientemente com blocos construtivos, e também com poucos recursos para se beneficiarem do conhecimento já adquirido acerca do espaço de busca. A segunda contribuição desta tese é a proposição de quatro algoritmos que procuram superar estas limitações, em contextos mono-objetivo e multiobjetivo. São substituídos os operadores de clonagem e mutação por um modelo probabilístico representando a distribuição de probabilidades das melhores soluções. Em seguida, este modelo é empregado para gerar novas soluções. Os modelos probabilísticos utilizados são a rede bayesiana, para espaços discretos, e a rede gaussiana, para espaços contínuos. A escolha de ambas se deve às suas capacidades de capturar adequadamente as interações mais relevantes das variáveis do problema. Resultados promissores foram obtidos nos experimentos de otimização realizados, os quais trataram, em espaços discretos, de seleção de atributos e de ensembles para classificação de padrões, e em espaços contínuos, de funções multimodais de elevada dimensão. Palavras-chave: sistemas imunológicos artificiais, redes bayesianas, redes gaussianas, otimização em espaços discretos e contínuos, otimização mono-objetivo e multiobjetivoAbstract: Artificial immune systems (AISs) and probabilistic graphical models are two important techniques for the design of intelligent systems, and they have been widely explored by researchers from diverse areas, in both theoretical and practical aspects. However, the potential of each technique is usually explored in isolation, without considering the possible cooperation between them. As a first contribution of this work, it is proposed an approach that explores the main advantages of AISs as optimization tools applied to the learning of Bayesian networks from data sets. On the other hand, the AISs already proposed to perform optimization in discrete and continuous spaces correspond to population-based meta-heuristics without mechanisms to deal effectively with building blocks, and also having few resources to benefit from the knowledge already acquired from the search space. The second contribution of this thesis is the proposition of four algorithms devoted to overcoming these limitations, both in single-objective and multi-objective contexts. The cloning and mutation operators are replaced by a probabilistic model representing the probability distribution of the best solutions. After that, this model is employed to generate new solutions. The probabilistic models adopted are the Bayesian network, for discrete spaces, and the Gaussian network, for continuous spaces. These choices are supported by their ability to properly capture the most relevant interactions among the variables of the problem. Promising results were obtained in the optimization experiments carried out, which have treated, in discrete spaces, feature selection and ensembles for pattern classification, and, in continuous spaces, multimodal functions of high dimension. Keywords: artificial immune systems, Bayesian networks, Gaussian networks, optimization in discrete and continuous domains, single-objective and multi-objective optimizationDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric

Inteligencia artificial, métodos bio-inspirados: un enfoque funcional para las ciencias de la computación

A lo largo de la historia del hombre la naturaleza, y los complejos procesos que se ven involucrados en ella, han servido de inspiración para resolver problemas de la vida cotidiana. Los algoritmos bio-inspirados son una rama de la Inteligencia Artificial en la que se emula el comportamiento de los sistemas naturales con el fin de diseñar métodos heurísticos no determinísticos de búsqueda, optimización, aprendizaje, reconocimiento, simulación y caracterización. Se puede pensar en la Inteligencia Artificial como una ciencia que trata de incorporar conocimiento a los procesos que realiza una máquina, para que estos se ejecuten con éxito. Uno de los personajes más influyentes en la actualidad en el campo de la Inteligencia Artificial es Ray Kurzweil, quien es considerado como un experto en el campo y es autor de The age of intelligent machines. Kurzweil asevera en sus escritos que para el año 2030 la Inteligencia Artificial logrará un avance extraordinario ya que superará a la inteligencia humana. Con esta afirmación hace mención a que las máquinas podrán igualar las capacidades del ser humano en el ámbito de laboratorio y dentro de 50 años lograrán formar parte de la vida cotidiana que vive el hombre. También Kurzweil ha mencionado que en los próximos 100 años existirán nuevas máquinas las cuales tendrán una mayor capacidad que en la actualidad, serán “superinteligentes”. La Inteligencia Artificial es entonces el fututo de la computación y de los sistemas artificiales que nos rodean. Los algoritmos bio-inspirados le imprimen naturalidad a dichos sistemas y poco a poco se irán refinando para asemejarse a aún más a los métodos utilizados por la naturaleza. El presente trabajo integra los fundamentos biológicos y matemáticos de los principales algoritmos bio-inspirados utilizados en las ciencias de la computación, así como algunas de sus principales aplicaciones. La importancia de estudiar estos algoritmos radica en que los métodos inspirados en el comportamiento biológico han probado ser eficaces en la solución de numerosos problemas de ingeniería que, si se aplicarán los métodos tradicionales, resultarían en problemas demasiado costosos de resolver