A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems

This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier's structure and the parameters of RBF kernels are determined using a PSO algorithm based on the criterion of minimising the leave-one-out misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

PSO assisted NURB Neural Network Identification

A system identification algorithm is introduced for Hammerstein systems that are modelled using a non-uniform rational B-spline (NURB) neural network. The proposed algorithm consists of two successive stages. First the shaping parameters in NURB network are estimated using a particle swarm optimization (PSO) procedure. Then the remaining parameters are estimated by the method of the singular value decomposition (SVD). Numerical examples are utilized to demonstrate the efficacy of the proposed approach.

Modeling and forecasting long-term natural gas (NG) consumption in Iran, using particle swarm optimization (PSO)

The gradual changes in the world development have brought energy issues back into high profile. An ongoing challenge for countries around the world is to balance the development gains against its effects on the environment. The energy management is the key factor of any sustainable development program. All the aspects of development in agriculture, power generation, social welfare and industry in Iran are crucially related to the energy and its revenue. Forecasting end-use natural gas consumption is an important Factor for efficient system operation and a basis for planning decisions. In this thesis, particle swarm optimization (PSO) used to forecast long run natural gas consumption in Iran. Gas consumption data in Iran for the previous 34 years is used to predict the consumption for the coming years. Four linear and nonlinear models proposed and six factors such as Gross Domestic Product (GDP), Population, National Income (NI), Temperature, Consumer Price Index (CPI) and yearly Natural Gas (NG) demand investigated.

Identificação e comparação entre controle preditivo com modelo não linear e PI sintonizados com PSO em sistema de separação gravitacional de águia-óleo

The separation methods are reduced applications as a result of the operational costs, the low output and the long time to separate the uids. But, these treatment methods are important because of the need for extraction of unwanted contaminants in the oil production. The water and the concentration of oil in water should be minimal (around 40 to 20 ppm) in order to take it to the sea. Because of the need of primary treatment, the objective of this project is to study and implement algorithms for identification of polynomial NARX (Nonlinear Auto-Regressive with Exogenous Input) models in closed loop, implement a structural identification, and compare strategies using PI control and updated on-line NARX predictive models on a combination of three-phase separator in series with three hydro cyclones batteries. The main goal of this project is to: obtain an optimized process of phase separation that will regulate the system, even in the presence of oil gushes; Show that it is possible to get optimized tunings for controllers analyzing the mesh as a whole, and evaluate and compare the strategies of PI and predictive control applied to the process. To accomplish these goals a simulator was used to represent the three phase separator and hydro cyclones. Algorithms were developed for system identification (NARX) using RLS(Recursive Least Square), along with methods for structure models detection. Predictive Control Algorithms were also implemented with NARX model updated on-line, and optimization algorithms using PSO (Particle Swarm Optimization). This project ends with a comparison of results obtained from the use of PI and predictive controllers (both with optimal state through the algorithm of cloud particles) in the simulated system. Thus, concluding that the performed optimizations make the system less sensitive to external perturbations and when optimized, the two controllers show similar results with the assessment of predictive control somewhat less sensitive to disturbances

Optimisation of a fermentation process for butanol production by particle swarm optimisation (PSO)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Kappa-PSO-ARTMAP Fuzzy: uma metodologia para detecção de intrusos baseado em seleção de atributos e otimização de parâmetros numa rede neural ARTMAP Fuzzy

Pós-graduação em Engenharia Elétrica - FEIS

The Extremely Red, Young L Dwarf PSO J318.5338-22.8603: a Free-Floating Planetary-Mass Analog to Directly Imaged Young Gas-Giant Planets

We have discovered using Pan-STARRS1 an extremely red late-L dwarf, which has (J - K)(MKO) = 2.78 and (J - K) (2MASS) = 2.84, making it the reddest known field dwarf and second only to 2MASS J1207-39b among substellar companions. Near-IR spectroscopy shows a spectral type of L7 +/- 1 and reveals a triangular H-band continuum and weak alkali (K I and Na I) lines, hallmarks of low surface gravity. Near-IR astrometry from the Hawaii Infrared Parallax Program gives a distance of 24.6 +/- 1.4 pc and indicates a much fainter J-band absolute magnitude than field L dwarfs. The position and kinematics of PSO J318.5-22 point to membership in the beta Pic moving group. Evolutionary models give a temperature of 1160(-40)(+30) K and a mass of 6.5(-1.0)(+1.3) M-Jup, making PSO J318.5-22 one of the lowest mass free-floating objects in the solar neighborhood. This object adds to the growing list of low-gravity field L dwarfs and is the first to be strongly deficient in methane relative to its estimated temperature. Comparing their spectra suggests that young L dwarfs with similar ages and temperatures can have different spectral signatures of youth. For the two objects with well constrained ages (PSO J318.5-22 and 2MASS J0355+11), we find their temperatures are approximate to 400 K cooler than field objects of similar spectral type but their luminosities are similar, i.e., these young L dwarfs are very red and unusually cool but not "underluminous." Altogether, PSO J318.5-22 is the first free-floating object with the colors, magnitudes, spectrum, luminosity, and mass that overlap the young dusty planets around HR 8799 and 2MASS J1207-39

PSO and neural networks: Optimal combination to solve non linear complex problems

Swarm colonies reproduce social habits. Working together in a group to reach a predefined goal is a social behaviour occurring in nature. Linear optimization problems have been approached by different techniques based on natural models. In particular, Particles Swarm optimization is a meta-heuristic search technique that has proven to be effective when dealing with complex optimization problems. This paper presents and develops a new method based on different penalties strategies to solve complex problems. It focuses on the training process of the neural networks, the constraints and the election of the parameters to ensure successful results and to avoid the most common obstacles when searching optimal solutions.

Debonding detection of FRP strengthened concrete beams by using impedance measurements and an ensemble PSO adaptive spectral model

An impedance-based midspan debonding identification method for RC beams strengthened with FRP strips is presented in this paper using piezoelectric ceramic (PZT) sensor?actuators. To reach this purpose, firstly, a two-dimensional electromechanical impedance model is proposed to predict the electrical admittance of the PZT transducer bonded to the FRP strips of an RC beam. Considering the impedance is measured in high frequencies, a spectral element model of the bonded-PZT?FRP strengthened beam is developed. This model, in conjunction with experimental measurements of PZT transducers, is used to present an updating methodology to quantitatively detect interfacial debonding of these kinds of structures. To improve the performance and accuracy of the detection algorithm in a challenging problem such as ours, the structural health monitoring approach is solved with an ensemble process based on particle of swarm. An adaptive mesh scheme has also been developed to increase the reliability in locating the area in which debonding initiates. Predictions carried out with experimental results have showed the effectiveness and potential of the proposed method to detect prematurely at its earliest stages a critical failure mode such as that due to midspan debonding of the FRP strip.

An automatic tuning strategy for local fuzzy logic ramp metering algorithm using Particle Swarm Optimization (PSO)

Freeway systems are becoming more congested each day. One contribution to freeway traffic congestion comprises platoons of on-ramp traffic merging into freeway mainlines. As a relatively low-cost countermeasure to the problem, ramp meters are being deployed in both directions of an 11-mile section of I-95 in Miami-Dade County, Florida. The local Fuzzy Logic (FL) ramp metering algorithm implemented in Seattle, Washington, has been selected for deployment. The FL ramp metering algorithm is powered by the Fuzzy Logic Controller (FLC). The FLC depends on a series of parameters that can significantly alter the behavior of the controller, thus affecting the performance of ramp meters. However, the most suitable values for these parameters are often difficult to determine, as they vary with current traffic conditions. Thus, for optimum performance, the parameter values must be fine-tuned. This research presents a new method of fine tuning the FLC parameters using Particle Swarm Optimization (PSO). PSO attempts to optimize several important parameters of the FLC. The objective function of the optimization model incorporates the METANET macroscopic traffic flow model to minimize delay time, subject to the constraints of reasonable ranges of ramp metering rates and FLC parameters. To further improve the performance, a short-term traffic forecasting module using a discrete Kalman filter was incorporated to predict the downstream freeway mainline occupancy. This helps to detect the presence of downstream bottlenecks. The CORSIM microscopic simulation model was selected as the platform to evaluate the performance of the proposed PSO tuning strategy. The ramp-metering algorithm incorporating the tuning strategy was implemented using CORSIM's run-time extension (RTE) and was tested on the aforementioned I-95 corridor. The performance of the FLC with PSO tuning was compared with the performance of the existing FLC without PSO tuning. The results show that the FLC with PSO tuning outperforms the existing FL metering, fixed-time metering, and existing conditions without metering in terms of total travel time savings, average speed, and system-wide throughput.

Algoritmo modificado de PSO matricial aplicado a identificação de sistemas com análise de convergência

Recentemente diversas técnicas de computação evolucionárias têm sido utilizadas em áreas como estimação de parâmetros de processos dinâmicos lineares e não lineares ou até sujeitos a incertezas. Isso motiva a utilização de algoritmos como o otimizador por nuvem de partículas (PSO) nas referidas áreas do conhecimento. Porém, pouco se sabe sobre a convergência desse algoritmo e, principalmente, as análises e estudos realizados têm se concentrado em resultados experimentais. Por isso, é objetivo deste trabalho propor uma nova estrutura para o PSO que permita analisar melhor a convergência do algoritmo de forma analítica. Para isso, o PSO é reestruturado para assumir uma forma matricial e reformulado como um sistema linear por partes. As partes serão analisadas de forma separada e será proposta a inserção de um fator de esquecimento que garante que a parte mais significativa deste sistema possua autovalores dentro do círculo de raio unitário. Também será realizada a análise da convergência do algoritmo como um todo, utilizando um critério de convergência quase certa, aplicável a sistemas chaveados. Na sequência, serão realizados testes experimentais de maneira a verificar o comportamento dos autovalores após a inserção do fator de esquecimento. Posteriormente, os algoritmos de identificação de parâmetros tradicionais serão combinados com o PSO matricial, de maneira a tornar os resultados da identificação tão bons ou melhores que a identificação apenas com o PSO ou, apenas com os algoritmos tradicionais. Os resultados mostram a convergência das partículas em uma região delimitada e que as funções obtidas após a combinação do algoritmo PSO matricial com os algoritmos convencionais, apresentam maior generalização para o sistema apresentado. As conclusões a que se chega é que a hibridização, apesar de limitar a busca por uma partícula mais apta do PSO, permite um desempenho mínimo para o algoritmo e ainda possibilita melhorar o resultado obtido com os algoritmos tradicionais, permitindo a representação do sistema aproximado em quantidades maiores de frequências.

Initial steps in optimal planning of a distribution system

This paper reports the initial steps of research on planning of rural networks for MV and LV. In this paper, two different cases are studied. In the first case, 100 loads are distributed uniformly on a 100 km transmission line in a distribution network and in the second case, the load structure become closer to the rural situation. In case 2, 21 loads are located in a distribution system so that their distance is increasing, distance between load 1 and 2 is 3 km, between 2 and 3 is 6 km, etc). These two models to some extent represent the distribution system in urban and rural areas, respectively. The objective function for the design of the optimal system consists of three main parts: cost of transformers, and MV and LV conductors. The bus voltage is expressed as a constraint and should be maintained within a standard level, rising or falling by no more than 5%.

Some novel techniques of parameter estimation for the dynamical models in biological systems

Inverse problems based on using experimental data to estimate unknown parameters of a system often arise in biological and chaotic systems. In this paper, we consider parameter estimation in systems biology involving linear and non-linear complex dynamical models, including the Michaelis–Menten enzyme kinetic system, a dynamical model of competence induction in Bacillus subtilis bacteria and a model of feedback bypass in B. subtilis bacteria. We propose some novel techniques for inverse problems. Firstly, we establish an approximation of a non-linear differential algebraic equation that corresponds to the given biological systems. Secondly, we use the Picard contraction mapping, collage methods and numerical integration techniques to convert the parameter estimation into a minimization problem of the parameters. We propose two optimization techniques: a grid approximation method and a modified hybrid Nelder–Mead simplex search and particle swarm optimization (MH-NMSS-PSO) for non-linear parameter estimation. The two techniques are used for parameter estimation in a model of competence induction in B. subtilis bacteria with noisy data. The MH-NMSS-PSO scheme is applied to a dynamical model of competence induction in B. subtilis bacteria based on experimental data and the model for feedback bypass. Numerical results demonstrate the effectiveness of our approach.

Train service timetabling in railway open markets by particle swarm optimisation

Railway timetabling is an important process in train service provision as it matches the transportation demand with the infrastructure capacity while customer satisfaction is also considered. It is a multi-objective optimisation problem, in which a feasible solution, rather than the optimal one, is usually taken in practice because of the time constraint. The quality of services may suffer as a result. In a railway open market, timetabling usually involves rounds of negotiations among a number of self-interested and independent stakeholders and hence additional objectives and constraints are imposed on the timetabling problem. While the requirements of all stakeholders are taken into consideration simultaneously, the computation demand is inevitably immense. Intelligent solution-searching techniques provide a possible solution. This paper attempts to employ a particle swarm optimisation (PSO) approach to devise a railway timetable in an open market. The suitability and performance of PSO are studied on a multi-agent-based railway open-market negotiation simulation platform.

Coupling hybrid-game strategies with particle swarm optimisation for multi-objective high lift systems design optimisation

This paper investigates the High Lift System (HLS) application of complex aerodynamic design problem using Particle Swarm Optimisation (PSO) coupled to Game strategies. Two types of optimization methods are used; the first method is a standard PSO based on Pareto dominance and the second method hybridises PSO with a well-known Nash Game strategies named Hybrid-PSO. These optimization techniques are coupled to a pre/post processor GiD providing unstructured meshes during the optimisation procedure and a transonic analysis software PUMI. The computational efficiency and quality design obtained by PSO and Hybrid-PSO are compared. The numerical results for the multi-objective HLS design optimisation clearly shows the benefits of hybridising a PSO with the Nash game and makes promising the above methodology for solving other more complex multi-physics optimisation problems in Aeronautics.