Particle Swarm Optimization PSO)

Evolutionary algorithms (EAs) have the potential to handle complex, multi-dimensional optimization problems in the field of phased array. Out of different EAs, particle swarm optimization (PSO) is a popular choice. In a phased array, antenna element failure is a common phenomenon and this leads to degradation of the array factor (AF) pattern, primarily in terms of increased side lobe levels (SLLs), displacement of nulls and reduction in the null depths. The recovery of a degraded pattern using a cost and time-effective approach is on demand. In this context, an attempt made to obtain an optimized AF pattern after fault in a 49 elements quasi-circular aperture equilateral triangular grid active planar phased array using PSO. In the paper, multiple cases on recovery are discussed having a maximum 20% element failure. Each recovery is also further evaluated by different statistical analyses. A dedicated software tool was developed to carry out the work presented in this paper. KEYWORDS...

The aim of this work is to design and analyze a Proportional Integral (PI) controller for Positive Output Luo Converter applications. Positive Output Luo Converter is a developed DC-DC converter. It is respected as a right choice for most industrial application where the rate of the output load voltage must be varying between the low and high values of the input value of voltage, output voltage rise and fall is smaller. This converter involve Power electronics switches (Diodes and MOSFET) since these elements are non-linear. The detailed model includes high-frequency switching that is introducing discontinuities into the model. PI controller coefficients (kp, ki) are calculated by particle swarm optimization (PSO) to provide optimal PI as hybrid PI by PSO controller with simple design procedure .Transient and steady state responses requirement of the system are considered in designing the proposed PI controller. The consequences show that the time of performing characteristics of PSO-PI controller established on integral squared error (ISE) performance index has the best time performing characteristics, line disturbance, load disturbance and set point variation.

One of the most important issues discussed in Wireless Sensor Networks (WSNs) is how to transfer information from nodes within the network to the base station and select the best possible route for transmission of this information, taking into account energy consumption for the network lifetime with maximum reliability and security. Hence, it would be useful to provide a suitable method that would have the features mentioned. This paper uses an Ad-hoc On-demand Multipath Distance Vector (AOMDV) as a routing protocol. This protocol has high energy consumption due to its multipath. However, it is a big challenge if it can reduce AOMDV energy consumption. Therefore, clustering operations for nodes are of high priority to determine the head of clusters which LEACH protocol and fuzzy logic and Particle Swarm Optimization (PSO) algorithm are used for this purpose. Simulation results represent 5% improvement in energy consumption in a WSN compared to AOMDV method.

—PSO is a way of optimizing a function without knowing the function by analyzing social behavior of particles in a swarm. In PSO every particle knows it's neighbor's best and will be move toward it; if the neighbor's best is better than its own best and this effects the velocity vector of a particle. Evaluation and comparison results shows that our algorithm achieves considerably higher average. In this paper, we combine parameters with the encoding space of a particle. By doing this personal best of each particle and finally the global best of whole swarm will improve remarkably.

The potentials of ecotourism for sustainable use of natural resources, poverty alleviation and food security was assessed in Awash National Park (ANP), Ethiopia using survey questionnaires and field observation between January to September 2013. The analyses of the study revealed that natural and cultural resources such as abundance and diversity of large wild mammals, bird, scenic landscape, hot springs, cultural and historical attractions and hotels or lodges at nearby areas are the main ecotourism potential in ANP. Deforestation, expansion of farming, overgrazing and over extraction of water coupled with conflicts between park and local communities are detected as the major problem contributors to the degradations of natural resources of the area. The study also revealed some income generating alternatives or ecotourism potentials which could help to reduce the present degradations of natural resources of the park. About 77.5% of sampled households expressed their interests for d...

This paper proposes a change point detection for electroencephalograms (EEG) signal application based on Particle Swarm Optimization (PSO). As EEG signal is well known consider as non-stationary in nature, we model the signal by using the sinusoidal-Heaviside function, which are capable to represent the change of the behavior of the signal. The parameter of the model with the change point location can be tuned by finding the minimum value of sum squared error. It was showed that the minimum value of sum squared error in the parameter tuning give the exact location of change point. The proposed method is applied to the human EEG during an eye moving task. © 2011 Springer-Verlag.

http://link.springer.com/chapter/10.1007/978-3-642-21729-6_122

A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN...

This paper focuses on the development of a prototype of thruster motor speed controller which exhibits robust performance for an Autonomous underwater vehicle. H infinity based speed controller with particle swarm optimized weights for a sensorless BLDC motor which is used as electrical thruster has been simulated in MATLAB/ SIMULINK and implemented using TI C2000 Delfino LaunchPad LaunchXL-F28377S and BoostXL DRV 8301. A performance comparison in reference tracking has been done with conventional PI controller and experimental results have been discussed in detail. The percentage variation in speed with respect to reference speed of proposed strategy has been observed to be 0.65% whereas it is 1.1% with PI controller. It has also been found that the proposed control strategy exhibits smooth starting with better reference tracking and less torque ripples.

Bu çalışmada, dalga enerji dönüştürücülerinden (DED) elde edilen enerji ile beslenen bir sabit mıknatıslı doğru akım motorunun (SMDAM) hız kontrolü, klasik kontrol yöntemlerinden farklı olarak ağırlık katsayılı oransal-integral denetleyici (AKOİK) yardımıyla MATLAB/Simulink ortamında gerçeklenmiştir. Ağırlık katsayılı oransal-integral denetleyicinin parametrelerin ideal değerleri (Kp, Ki, Ke) parçacık sürü optimizasyonu yardımıyla, zaman ağırlıklı mutlak hatanın integralini minimum yapacak şekilde aratılmıştır. Elde edilen sonuçlar yorumlanarak değerlendirilmiştir.

ARTICLE HISTORY Introduction In order to achieve sustainable management of water resources, an integrated knowledge of water resources and modeling are essential, especially in arid and semi-arid regions where water resources have become scarcer with increasing demands from socioeconomic development and population growth. In recent years, utilization of hydrological models has been increased to simulate watershed processes and for cost saving. Various hydrological models such as soil and water assessment tool (SWAT) have been developed to simulate runoff in the watersheds. In this study, the SWAT was used to simulate monthly runoff in the Bazoft watershed and the impact of springs discharge on the simulation accuracy was evaluated. Materials and Methods Bazoft is one of the watersheds in Karun basin, (31° 37′ to 32° 39′ N and 49° 34′ to 50° 32′ E) located in northern part of the Karun river basin in southwestern Iran. The area of the watershed is 2168 km2. The main river in the watershed is Ab Bazoft which is joined by Karun River at the outlet of the watershed. The elevation ranges from 880 m at the southern of the watershed to 4200 m on Zardkuh Mountain in the north eastern. Because the topography is very high in the watershed, the rainfall distribution is different, the average of rainfall in northern part of the watershed is 1400 mm, while the southern part is 500mm. The SWAT model was used because it is a continuous time, spatially and semi-distributed and basin-scale model, in which hydrological processes and water quality are coupled with crop growth and agricultural management practices. Input data include Digital Elevation Model (DEM), land use, soil type, meteorological and hydrological observed data were provided. After running the model, a sensitivity analysis was done using the one at time method and SUFI-2 program. For calibration and uncertainty analysis in this study, we used the PSO (particle swarm optimization) algorithm. SUFI-2 and PSO are linked to SWAT in environment of SWAT-CUP software. We used about two-third of the observed data for calibration and the remaining for validation. The simulation period was from 1992 to 2008. The study period was 1998–2008 for calibration and 1992–1997 for validation. The calibration and validation period results were analyzed at monthly time scale.). The objective function was the

This paper proposed a technique to identify Proton Exchange Membrane Fuel Cell (PEMFC)'s parameters using an optimization approach. This paper present the application of particle swarm optimization (PSO) based algorithm known as local optima avoidance particle swarm optimization (LOAPSO) in extracting parameters from PEMFC. The proposed technique is used to identify the PEMFC parameters in term of the voltage-current characteristics. An experimental data from publish literature are used to test and verify the consistency of accurately identifying various parameters.

In cancer classification, gene selection is an important data preprocessing technique, but it is a difficult task due to the large search space. Accordingly, the objective of this study is to develop a hybrid meta-heuristic Binary Black Hole Algorithm (BBHA) and Binary Particle Swarm Optimization (BPSO) (4-2) model that emphasizes gene selection. In this model, the BBHA is embedded in the BPSO (4-2) algorithm to make the BPSO (4-2) more effective and to facilitate the exploration and exploitation of the BPSO (4-2) algorithm to further improve the performance. This model has been associated with Random Forest Recursive Feature Elimination (RF-RFE) pre-filtering technique. The classifiers which are evaluated in the proposed framework are Sparse Partial Least Squares Discriminant Analysis (SPLSDA); k-nearest neighbor and Naive Bayes. The performance of the proposed method was evaluated on two benchmarks and three clinical microarrays. The experimental results and statistical analysis c...

An intelligent control of Doubly Fed Induction Generator (DFIG) system using Proportional-Integral (PI)controller tuned by optimization techniques is proposed in this paper.System identification technique was presented in this work to estimate the transfer function of the reactive power loop and speed loop of the proposed system.An implemented laboratory prototype consists of 0.37kW, 220 V, 50Hz Brushless DC Motor (BLDC) and its drive circuit controlled by voltage source inverter for various wind speed.A 0.27 kW wound rotor induction machine, working as the DFIG, coupled with turbine machine by a coupler and driven through a back-to-back converter. This system can be applied as a stand-alone power supply system or as the emergency power system when the electricity grid fails. The rotor side converter is controlled using the field-oriented control to control the reactive power at different rotor speeds.Grey Wolf Optimizer (GWO) proposed in this study to tune the (PI) controller. More...

The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view of customer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfaction data taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value is AUC of 0.923.

Dynamic Transmission Expansion Planning (DTEP) seeks to answer where, how many, and when new infrastructure should be added to the electrical system. The goal is to allow a correct and efficient operation along the planning horizon. In this research work, a load shedding formulation extended for the multistage alternating current (AC) model with the co-optimization of shunt compensation is proposed. A novel hybrid meta-heuristic is used as optimization technique to solve the DTEP problem. Solutions were obtained from IEEE 24-bus test system. For comparative purposes, the problem was also solved using static (STEP) and quasi-dynamic (QTEP) approaches.

In scientific visualization, we often handle vector or direction fields. An efficient way of visualizing such a vector or direction field is to cover the whole domain studied by a set of streamlines because they give good information about the flow and the topology of the underlying field. In this article, we propose to optimize already constructed streamlines by defining an optimization criterion related to the length of the line. We propose to use the Particle Swarm Optimization (PSO) method. It makes it possible by inducing the results to converge to the optimum of the objective function related to the defined problem. The obtained results are encouraging and merit continuity in the enrichment of the objective function.

Real-time energy management of a converter-based microgrid is difficult to determine optimal operating points of a storage system in order to save costs and minimise energy waste. This complexity arises due to time-varying electricity prices, stochastic energy sources and power demand. Many countries have imposed real-time electricity pricing to efficiently control demand side management. This paper presents a particle swarm optimisation (PSO) for the application of real-time energy management to find optimal battery controls of a community microgrid. The modification of the PSO consists in altering the cost function to better model the battery charging/discharging operations. As optimal control is performed by formulating a cost function, it is suitably analysed and then a dynamic penalty function in order to obtain the best cost function is proposed. Several case studies with different scenarios are conducted to determine the effectiveness of the proposed cost function. The proposed cost function can reduce operational cost by 12% as compared to the original cost function over a time horizon of 96 hours. Simulation results reveal the suitability of applying the regularised PSO algorithm with the proposed cost function, which can be adjusted according to the need of the community, for real-time energy management. Keywords: Converter-based microgrids, renewable energy sources, optimum battery control, real-time energy management, and particle swarm optimisation. Abbreviations The following abbreviations are used in this manuscript: PSO Particle swarm optimisation RESs Renewable energy sources ESS Energy storage system SOC State of charge ADP Adaptive dynamic programming I-DEMS Intelligent dynamic energy management system PV Photovoltaic PCC Point of common-coupling EMS Energy management system BL o Initial battery energy level BL max Maximum battery energy level BL min Minimum battery energy level P wT Total wind power P sT Total solar power P g Import/export grid power L(t) Load at time t 5 *

Machine Intelligence plays a crucial role in the design of expert systems in medical diagnosis. In India most of the people suffering from some sort of diseases like asthma, diabetics, cancer and many more. We consider the disease asthma for diagnosis. The diagnosis of asthma can be done in two ways 1) through questionnaire and 2) through clinical data. We considered both approaches to design the expert system for diagnosis of asthma. We have chosen some machine learning algorithms such as Context sensitive auto-associative memory neural network model[1] (CSAMM), Backpropogation model, C4.5 algorithm, Bayesian Network, Particle Swarm Optimization [7]. We present a performance study on these algorithms in terms of accuracy and some outstanding characteristics.

Employing Resistive Superconducting Fault Current Limiters (RSFCL) is one of the practical and effective methods to improve the transient stability of a power system by limiting the fault current. Regarding technical and economical constraints, optimal sizing and allocation of RSFCLs in a power system is of significant importance. It is the purpose of this paper to propose an algorithm based on the Particle Swarm Optimization (PSO) in order to improve the transient stability of a power system by optimal sizing and allocation of RSFCLs. The proposed algorithm is next applied to the New England 39-bus test system as a case study and the results are simulated in Matlab. Simulation results reveal that in the case of employing RSFCLs with sizes and locations resulted from the optimization algorithm, the transient stability of the power system under study is improved. Furthermore, it seems that the optimal locations of RSFCLs are to some extent near the fault location. KEYWORDS Transient Stability Improvement, Resistive Superconducting Fault Current Limiters (RSFCL), Particle Swarm Optimization (PSO)

With the increasing levels of Electromagnetic pollution almost exponentially in this modern age of Electronics reported and highlighted by numerous studies carried out by scientists from all over the world, inspire engineers to concentrate their research for the optimum design of multilayer microwave absorber considering various parameters which are inherently conflicting in nature. In this paper we mainly focus on the comparative study of different Evolutionary algorithms for the optimum design of thin broadband (2-20GHz) multilayer microwave absorber for oblique incidence (30 0) considering arbitrary polarization of the electromagnetic waves. Different models are presented and synthesized using various Evolutionary algorithm namely Firefly algorithm (FA), Particle swarm optimization (PSO), Artificial bee colony optimization (ABC) and the best simulated results are tabulated and compared with each others.  KEYWORDS Multilayer microwave absorber, oblique incidence, TE polarization, TM polarization, Particle swarm optimization (PSO), Firefly algorithm (FA), Artificial bee colony optimization (ABC).

The objective of this paper is to reduce the power loss and to improve the voltage profile in radial distribution system. Fuzzy and particle swarm optimization algorithm is a two-stage methodology used for placement and sizing of UPFC in this paper. To show the effectiveness of the same a complete result analysis is carried out on 33 and 69 bus systems. Power loss and voltages are calculated for the optimal locations based on fuzzy system. 50%, 80% 150%, overloading cases are also considered in this paper. The result analysis shows that the two-stage methodology effectively improves the voltages and reduces the power loss of the system.

International Journal of Fuzzy Logic Systems (IJFLS) is an open access peer-reviewed journal that covers all topics in theoretical, experimental and applied fuzzy techniques and systems. It is aimed to bring together researchers and developers from both academia and industry to discuss the latest scientific and theoretical advances in this field, and to demonstrate the state-of-the-art systems. The journal solicits original technical papers that were not previously published and are not currently under review for publication elsewhere.

The prediction and analysis of stock market data have gotten a vital role in today's economy. The financial prediction or in particular stock market prediction is one of the hottest fields of research lately due to its commercial applications owing to high stakes and the kinds of temptations gain that it has to offer. In this paper, we are using two types of models and compared those using historical data from the Bombay Stock Exchange. The models used were based on firstly, the Deep learning parameters updated through particle swarm optimization and secondly, the Deep learning parameters updated through the least mean square and data taken from Bombay Stock Exchange (BSE). The Deep learning algorithms vary extensively in the preference of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the procedure of data representation. Its capability to extract features from a huge set of raw data without believing on earlier knowledge of predictors makes deep learning potentially tempting for Bombay stock market prediction at high frequencies. The predicting the Bombay stock price of a company based on the historical value day to day open, high, low, close and volume of the related index. The outcome of the experiment has been depicted with the assistance of appropriate curves where a comparative analysis of the two types of models is done on the basis of several parameters. This prediction model is used with some general indicators to maximize the return and reduce the risk of the stock market.From the outcome obtained, it is understandable that the deep neural network models are competent of recognizing the patterns existing in the Bombay stock markets.

This paper deals with an analysis and implementation of a control method proposed in the maximum power point tracking (MPPT) for photovoltaic systems. The Improved Particle Swarm Optimization (IPSO) algorithm is developed and implemented in Matlab/Simulink environment. Many simulations have been done considering the different system responses as the current, voltage and essentially the power. The efficiency of the proposed MPPT algorithm have been carried out. Index Terms-Maximum Power Point Tracking (MPPT); Particle Swarm Optimization algorithm (PSO); Photovoltaic (PV); optimum duty cycle.

This paper presents an optimized reconfigurable control design methodology by separating control commands distribution task from flight controller for different types of fault handling. The proposed strategy improves the flight control performance in normal and fault situations. The particle swarm optimization (PSO) based multi-input multi-output (MIMO) linear quadratic regulator (LQR) is used to produce virtual command signals. A modified weighted pseudo-inverse (WPI) based cascaded reallocation technique is employed for effective implementation of commands to redundant control surfaces in a realistic nonlinear aircraft benchmark model. Control surface fault modelling is performed for the evaluation of optimized reconfiguration based modular flight control strategy. Simulation results show that acceptable fault tolerant control (FTC) performance can be achieved by using swarm intelligence based optimization technique for modular control design.

Brain Computer Interface (BCI) research is based on recording and analyzing electroencephalographic (EEG) data and recognizing EEG patterns associated with various mental states. BCIs had become an active research area in the last decade. The ability of The Multilayer Perceptron Neural Networks (MLPNN) to model non-linear relationship is appropriate to model the complex nature of EEG signals. MLPNN’s convergence rate is relatively slow and it often yields sub-optimal solutions. In this paper, it is proposed to enhance the classification ability of the MLPNN incorporating Particle Swarm Optimization (PSO). This optimization helps to overcome the slow convergence rate and to avoid local minimum. The features of motor imagery in the frequency domain are extracted using Hilbert transform, and Principal Component Analysis is used for feature reduction. This is classified by MLPNN modified with PSO.

In this paper, an optimal design of linear phase digital high pass (HP) finite impulse response (FIR) filter has been proposed using Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach and Wavelet Mutation (PSOCFIWA-WM). PSOCFIWA-WM incorporates a new definition of swarm updating in PSOCFIWA with the help of wavelet based mutation. Wavelet mutation enhances the PSOCFIWA to explore the solution space more effectively. In the design process, filter length, pass band and stop band edge frequencies, feasible pass band and stop band ripple sizes are specified. Other evolutionary algorithms like real coded genetic algorithm (RGA), particle swarm optimization (PSO), PSO with constriction factor and inertia weight approach (PSOCFIWA), and the proposed PSOCFIWA-WM have been used for the comparative design of linear phase FIR HP filter. A comparison of simulation results reveals the optimization superiority of the proposed technique over the other optimization techniques for the solution of FIR HP filter design.

"This paper proposes an improved maximum power
point tracking (MPPT) method for the photovoltaic (PV) system
using a modified particle swarm optimization (PSO) algorithm.
The main advantage of the method is the reduction of the steadystate
oscillation (to practically zero) once the maximum power
point (MPP) is located. Furthermore, the proposedmethod has the
ability to track the MPP for the extreme environmental condition,
e.g., large fluctuations of insolation and partial shading condition.
The algorithm is simple and can be computed very rapidly; thus,
its implementation using a low-cost microcontroller is possible. To
evaluate the effectiveness of the proposed method, MATLAB simulations
are carried out under very challenging conditions, namely
step changes in irradiance, step changes in load, and partial shading
of the PV array. Its performance is compared with the conventional
Hill Climbing (HC) method. Finally, an experimental rig
that comprises of a buck–boost converter fed by a custom-designed
solar array simulator is set up to emulate the simulation. The software
development is carried out in the Dspace 1104 environment
using a TMS320F240 digital signal processor. The superiority of
the proposed method over the HC in terms of tracking speed and
steady-state oscillations is highlighted by simulation and experimental
results."
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6138329&tag=1

Micro-electric discharge machining is a non-conventional electrothermal process used to produce microfeatures with precision and accuracy on difficult-to-cut materials. Efforts are made all over the globe for making the µ-EDM process robust for application through optimization. An attempt has been made to optimize the machining parameters of µ-EDM process using JAYA, teaching-learning-based optimization and novel multi-objective optimization techniques. To assess the performance of these algorithms, the results are compared with particle swarm optimization technique. The work focused on drilling of high-aspect-ratio microholes using brass electrode of 290 µm on Stainless Steel 317L (SS317L) as base material and deionized water as dielectric in an advanced EDM drill machine while minimizing surface roughness and overcut simultaneously. Optimum condition was produced at pulse-on time of 3.0776 µs, current of 1 A, servo voltage of 3 V and gap voltage of 30 V. JAYA algorithm produced optimum condition in a smaller number of generations compared to TLBO and PSO. Results showed that JAYA, TLBO and PSO are effective tools to obtain optimum condition for input parameters.

This paper introduces modelling and simulation of Doubly-Fed Induction Generator (DFIG) of Wind Energy Conversion System (WECS). Two Pulse Width Modulation (PWM) converters have been connected back to back from the rotor terminals to the utility grid via a dc-link. Vector control system typically controlled by a set of PI controllers, which have an important effect on the performance of system dynamics. This paper presents an optimally tuned PI controllers design of a DFIG wind energy system connected to grid using Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO). PSO and GWO used to optimize PI controller parameters of both Grid side converter (GSC), and Rotor side converter (RSC) to improve the dynamic operation of the DFIG wind energy system under a variable speed condition. Keywords-Doubly fed induction generator (DFIG), wind energy conversion system (WECS), Rotor side converter (RSC), Grid side converter (GSC), Particle Swarm Optimization (PSO), Grey Wolf Optimi...