Particle Swarm Optimization PSO)

Warehouses constitute a key component of supply chain networks. An improvement to the operational efficiency and the productivity of warehouses is crucial for supply chain practitioners and industrial managers. Overall warehouse efficiency largely depends on synergic performance. The managers preemptively estimate the overall warehouse performance (OWP), which requires an accurate prediction of a warehouse's key performance indicators (KPIs). This research aims to predict the KPIs of a ready-made garment (RMG) warehouse in Bangladesh with a low forecasting error in order to precisely measure OWP. Incorporating advice from experts, conducting a literature review, and accepting the limitations of data availability, this study identifies 13 KPIs. The traditional grey method (GM)-the GM (1, 1) model-is established to estimate the grey data with limited historical information but not absolute. To reduce the limitations of GM (1, 1), this paper introduces a novel particle swarm optimization (PSO)-based grey model-PSOGM (1, 1)-to predict the warehouse's KPIs with less forecasting error. This study also uses the genetic algorithm (GA)-based grey model-GAGM (1, 1)-the discrete grey model-DGM (1, 1)-to assess the performance of the proposed model in terms of the mean absolute percentage error and other assessment metrics. The proposed model outperforms the existing grey models in projecting OWP through the forecasting of KPIs over a 5-month period. To find out the optimal parameters of the PSO and GA algorithms before combining them with the grey model, this study adopts the Taguchi design method. Finally, this study aims to help warehouse professionals make quick OWP estimations in advance to take control measures regarding warehouse productivity and efficiency.

Photovoltaic (PV) system isa renewable form of energy, using direct sunlight and converting it into electrical power PV cells which are coupled as an array to generate usable electrical energy constitute the most critical parts of this system. Electronic converters are required to transform the output of system current &voltage into an appropriate form if consider the situation of system load & its requirements. The electronic converter more typically employed is a DC-DC converter with a solar cell low voltage generating high voltage. This paper looks at the DC/DC converters & PV system with references to both cases: the first case is, The design of the system as a loop system closed in the first case because the system's scenario is dependent on an different types of algorithm separately for MPPT, that captures the sunlight higher amount to produce the highest optimized electrical power. Although the system was created with MPPT in mind, the simulation was carried out with different a controller such as P&O, PSO, Inc and fuzzy logic. The simulation& execution results for such instances are shown to demonstrate the ability of o/p voltage to return to steady-state if the input voltage impact changed. There is also evidence of a brief settling time & overshoot in the output voltage return and comparative result shown that PSO and fuzzy algorithm found accepted results means best result compassion with the existing algorithm. This optimization was carried out with the assistance of MATLAB 2018(a)

Hybrid energy system provides alternative solution for better use in renewable energy system. The use of electric vehicle technology plays a crucial role in playing solution for environmental concern. This work provides fuzzy controller based hybrid energy management system of battery for electric vehicle. Some intelligent techniques are used for optimized the performance of system. First part of system describes the battery with supercapacitor with PV array and then second part describes the fuzzy based controlling system for monitoring the state of battery. The use of fuzzy provides the reduction of any other conventional method in system. All simulations are done with MATLAB/Simulink Model.

Two modern optimization methods including Particle Swarm Optimization and Differential Evolution are compared on twelve constrained nonlinear test functions. Generally, the results show that Differential Evolution is better than Particle Swarm Optimization in terms of high-quality solutions, running time and robustness.

This paper considers a multi-depot vehicle routing problem with pickup and delivery requests. In the problem of interest, each location may have goods for both pickup and delivery
with multiple delivery locations that may not be the depots. These characteristics are quite common in industrial practice. A particle swarm optimization algorithm with multiple social learning structures is proposed for solving the practical case of multi-depot vehicle routing problem with simultaneous pickup and delivery and time window. A new decoding procedure is
implemented using the PSO class provided in the ETLib object library. Computational experiments are carried out using the test instances for the pickup and delivery problem with
time windows (PDPTW) as well as a newly generated instance. The preliminary results show that the proposed algorithm is able to provide good solutions to most of the test problems

Photovoltaic (PV) system isa renewable form of energy, using direct sunlight and converting it into electrical power PV cells which are coupled as an array to generate usable electrical energy constitute the most critical parts of this system. Electronic converters are required to transform the output of system current &voltage into an appropriate form if consider the situation of system load & its requirements. The electronic converter more typically employed is a DC-DC converter with a solar cell low voltage generating high voltage. This paper looks at the DC/DC converters & PV system with references to both cases: the first case is, The design of the system as a loop system closed in the first case because the system's scenario is dependent on an different types of algorithm separately for MPPT, that captures the sunlight higher amount to produce the highest optimized electrical power. Although the system was created with MPPT in mind, the simulation was carried out with different a controller such as P&O, PSO, Inc and fuzzy logic. The simulation& execution results for such instances are shown to demonstrate the ability of o/p voltage to return to steady-state if the input voltage impact changed. There is also evidence of a brief settling time & overshoot in the output voltage return and comparative result shown that PSO and fuzzy algorithm found accepted results means best result compassion with the existing algorithm. This optimization was carried out with the assistance of MATLAB 2018(a)

For analysis & optimization purposes, it is necessary to represent an airfoil with fewer
parameters. In this paper, the two-dimensional surface of an airfoil has been represented by 5th order
Bézier curve. So, each of the upper and lower surface of the airfoil can be represented by (5+1) = 6 control
points only. For optimization purposes, control points of the cubic B-spline are used for modeling the airfoil
as analyses are done with Qblade. Here, the design space has been defined as the 25% above and 25%
below the y coordinates of the control points. Within this design space, two optimized shapes are obtained
by using Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) tool of Matlab respectively.
Each shape has a higher coefficient of lift-to-drag ratio (Cl/Cd) than that of the original airfoil for a range
of angle of attack (AoA). So, these two shapes can definitely be used in various aerodynamic applications
like in wind turbine and in aircraft wings to get better lift and reduced amount of drag force.

This project presents optimal design of digital FIR and IIR filters using evolutionary optimization methods. Some evolutionary optimization methods named as Normal Particle Swarm Optimization (PSO) , PSO with Constriction Factor and Inertia Weight Approach (PSOCFIWA), PSOCFIWA with Wavelet Mutation (PSOCFIWA-WM), Harmonic Search (HS), and Harmonic Search with Wavelet Mutation (HS-WM) are discussed in this project work and have been used for the optimal design of digital low pass, high pass, band pass and band stop filters. In order to show the comparative effectiveness of the discussed algorithms, the simulation results have been compared with the already existing well established results. Further to demonstrate the efficacy of the proposed methods, these have been implemented via simulink models in MATLAB.

As additive manufacturing rapidly expands the number of materials including waste plastics and composites, there is an urgent need to reduce the experimental time needed to identify optimized printing parameters for novel materials. Computational intelligence (CI) in general and particle swarm optimization (PSO) algorithms in particular have been shown to accelerate finding optimal printing parameters. Unfortunately, the implementation of CI has been prohibitively complex for noncomputer scientists. To overcome these limitations, this article develops, tests, and validates PSO Experimenter, an easy-to-use open-source platform based around the PSO algorithm and applies it to optimizing recycled materials. Specifically, PSO Experimenter is used to find optimal printing parameters for a relatively unexplored potential distributed recycling and additive manufacturing (DRAM) material that is widely available: low-density polyethylene (LDPE). LDPE has been used to make filament, but in this study for the first time it was used in the open source fused particle fabrication/fused granular fabrication system. PSO Experimenter successfully identified functional printing parameters for this challenging-to-print waste plastic. The results indicate that PSO Experimenter can provide 97% reduction in research time for 3D printing parameter optimization. It is concluded that the PSO Experimenter is a user-friendly and effective free software for finding ideal parameters for the burgeoning challenge of DRAM as well as a wide range of other fields and processes.

Hybrid Metaheuristics Optimization have emerged along the paradigm itself. Now, they are very famous because the hybrid metaheuristics methods give best results for combinatorial optimization problems compared to exact methods. In this paper, we will propose Metaheuristic method which are applied to difficult problems. This method is based on hybridization between population based solution methods like Ant colony optimization (ACO) and standard Particle swarm optimization (SPSO) algorithms and single based solution methods like 2-Opt algorithm. Our developed approach is called " Standard Ant Supervised by PSO " (SAS-PSO-2Opt) applied to routing problem like Traveling Salesman Problem (TSP), which is considered as NP-complete problem. Therefore, the ACO algorithm can explore the search space, PSO algorithm is used to optimize the ACO parameters (α, β, ρ) and the 2-Opt algorithm improves the obtained solution and reduce the probability of falling into a local minimum. To evaluate our proposed hybrid approach, we have used several standard tests benches from TSPLIB and we have compared the results with other hybrid metaheuristics approaches from litterature.

The power system blackout history of last two decades is presented.Conventional load shedding techniques, their types and limitations are presented.Applications of intelligent techniques in load shedding are presented.Intelligent techniques include ANN, fuzzy logic, ANFIS, genetic algorithm and PSO.The discussion and comparison between these techniques are provided.Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address this issue, reliable techniques are required to provide fast and accurate load shedding to prevent collapse in the power system. Computational intelligence techniques, due to their robustness and flexibility in dealing with complex non-linear systems, could be an option in addressing this problem. Computational intelligence includes techniques like artificial neural networks, genetic algorithms, fuzzy logic control, adaptive neuro-fuzzy inference system, and particle swarm optimization. Research in these techniques is being undertaken in order to discover means for more efficient and reliable load shedding. This paper provides an overview of these techniques as applied to load shedding in a power system. This paper also compares the advantages of computational intelligence techniques over conventional load shedding techniques. Finally, this paper discusses the limitation of computational intelligence techniques, which restricts their usage in load shedding in real time.

Nature is a great source of inspiration for solving complex problems in networks. It helps to find the optimal solution.
Metaheuristic algorithm is one of the nature-inspired algorithm which helps in solving routing problem in networks. The dynamic features, changing of topology frequently and limited bandwidth make the routing, challenging in MANET. Implementation of appropriate routing algorithms leads to the efficient transmission of data in mobile ad hoc networks. The algorithms that are inspired by the principles of naturally-distributed/collective behavior of social colonies have shown excellence in dealing with complex optimization problems. Thus some of the bio-inspired metaheuristic algorithms help to increase the efficiency of routing in ad hoc networks. This survey work presents the overview of bio-inspired metaheuristic algorithms which support the efficiency of routing in mobile ad hoc networks.

Accurate and fast islanding detection of distributed generation is highly important for its successful operation
in distribution networks. Up to now, various islanding detection technique based on communication,
passive, active and hybrid methods have been proposed. However, each technique suffers from
certain demerits that cause inaccuracies in islanding detection. Computational intelligence based techniques,
due to their robustness and flexibility in dealing with complex nonlinear systems, is an option
that might solve this problem. This paper aims to provide a comprehensive review of computational
intelligence based techniques applied for islanding detection of distributed generation. Moreover, the
paper compares the accuracies of computational intelligence based techniques over existing techniques
to provide a handful of information for industries and utility researchers to determine the best method
for their respective system.

The aim of this paper is to employ fractional order proportional integral derivative (FO-PID) controller and integer order PID controller to control the position of the levitated object in a magnetic levitation system (MLS), which is inherently nonlinear and unstable system. The proposal is to deploy discrete optimal pole-zero approximation method for realization of digital fractional order controller. An approach of phase shaping by slope cancellation of asymptotic phase plots for zeros and poles within given bandwidth is explored. The controller parameters are tuned using dynamic particle swarm optimization (dPSO) technique. Effectiveness of the proposed control scheme is verified by simulation and experimental results. The performance of realized digital FO-PID controller has been compared with that of the integer order PID controllers. It is observed that effort required in fractional order control is smaller as compared with its integer counterpart for obtaining the same system performance.

This paper provides an introduction and a comparison of two widely used evolutionary computation algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) based on the previous studies and researches. It describes Genetic Algorithm basic functionalities including various steps such as selection, crossover, and mutation.

Flexible manipulators are very commonly used in industries. In this paper a single-link flexible joint robot is modeled firstly by using Euler–Lagrange energy equation. An optimized Linear Quadratic Regulator is employed to control the manipulator. After that, a Linear Quadratic Regulator (LQR) controller is used for optimal control of the manipulator. For optimizing the LQR, the regulator term weighting of the LQR is achieved by using the newly introduced grey wolf optimizer technique. With the optimized LQR controller based on the proposed performance index, it is tried to have a system with the minimum overshoot and settling time. By considering the proposed performance index and comparing with the PSO-based controller as a popular algorithm, the superiority of the proposed LQR controller in improving the stability and performance of the manipulator is shown. The simulations are performed in MATLAB environment and the results confirm the efficiency of the proposed controller.

Several cases of service attacks on major Internet sites have shown us, no open computer network is immune from intrusions. The wireless ad-hoc network is particularly vulnerable due to its features of open medium, dynamic changing topology, cooperative algorithms, lack of centralized monitoring and management point, and lack of a clear line of defense. The traditional way of protecting networks with firewalls and encryption software is no longer sufficient and effective. The field of machine learning has proved that the hybridization of classifiers usually has a better performance than individual ones. This paper proposes a new approach that hybridizes different classifiers for better accuracy in detection of intrusion attacks. The result of experiments conducted shows that the fusion of Support Vector Machine, k-Nearest Neighbor, and Primal-Dual Particle Swarm Optimization produced better classification accuracy than each of the singular classifiers on the KDD99 dataset.

Since the efficiency of photovoltaic (PV) power is closely related to the weather, many PV enterprises install weather instruments to monitor the working state of the PV power system. With the development of the soft measurement technology, the instrumental method seems obsolete and involves high cost. This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data. By this method, the weather types are deduced by data analysis, instead of weather instrument. A better fault detection is obtained by using the support vector machines (SVM) and comparing the predicted and the actual weather. The model of the weather prediction is established by a direct SVM for training multiclass predictors. Although SVM is suitable for classification, the classified results depend on the type of the kernel, the parameters of the kernel, and the soft margin coefficient, which are difficult to choose. In this paper, these parameters are optimized by particle swarm optimization (PSO) algorithm in anticipation of good prediction results can be achieved. Prediction results show that this method is feasible and effective.

Travelling salesman problem (TSP) is a most popular combinatorial routing problem, belongs to the class of NP-hard problems. Many approacheshave been proposed for TSP.Among them, swarm intelligence (SI) algorithms can effectively achieve optimal tours with the minimum lengths and attempt to avoid trapping in local minima points. The transcendence of each SI is depended on the nature of the problem. In our studies, there has been yet no any article, which had compared the performance of SI algorithms for TSP perfectly. In this paper,four common SI algorithms are used to solve TSP, in order to compare the performance of SI algorithms for the TSP problem. These algorithms include genetic algorithm, particle swarm optimization, ant colony optimization, and artificial bee colony. For each SI, the various parameters and operators were tested, and the best values were selected for it. Experiments oversome benchmarks fromTSPLIBshow that artificial bee colony algorithm is the best one among the fourSI-basedmethods to solverouting problems like TSP.

Interval type-2 fuzzy logic systems (IT2FLSs), have recently shown great potential in various applications with dynamic uncertainties. It is believed that additional degree of uncertainty provided by IT2FL allows for better representation of the uncertainty and vagueness present in prediction models. However, determining the parameters of the membership functions of IT2FL is important for providing optimum performance of the system. Particle Swarm Optimization (PSO) has attracted the interest of researchers due to their simplicity, effectiveness and efficiency in solving real-world optimization problems. In this paper, a novel optimal IT2FLS is designed, applied for predicting winning chances in elections. PSO is used as an optimized algorithm to tune the parameter of the primary membership function of the IT2FL to improve the performance and increase the accuracy of the IT2F set. Simulation results show the superiority of the PSO-IT2FL to the similar non-optimal IT2FL system with an increase in the prediction.

Accurate simulation of evaporation plays an important role in the efficient management of water the direct method where Class A pan-evaporimeter is used, and an indirect method that includes empirical equations. However, despite its widespread usage, Class A pan-evaporimeter method can be affected by human and instrumentation errors. Empirical equations, on the other hand, are generally linked to the different climatic factors that should provide initial or boundary conditions in the mathematical equations that affect the rate of evaporation. Considering these challenging, heuristic soft computing approaches that do not need key information about the physics of evaporation. In this study, a Quantum-behaved Particle Swarm Optimization algorithm, embedded into a multi-layer perceptron technique, is developed to estimate the evaporation rates over a daily forecast horizon. The measured evaporation data from 2012–2014 for Talesh meteorological station located in Northern Iran are employed. The predictive accuracy of the MLP-QPSO model is evaluated with existing methods: i.e. a hybrid MLP-PSO and a standalone MLP model. The results are evaluated in respect to statistical performance criterion: the mean absolute error, root mean square error (RMSE), Willmott’s Index and the Nash–Sutcliffe coefficient. In conjunction with these metrics, Taylor diagrams are also utilized to assess the level of agreement between the forecasted and observed evaporation data. Evidently, the hybrid MLP-QPSO model is confirmed to be an optimal forecasting tool applied for estimating daily pan evaporation, outperforming both the hybrid MLP-PSO and the standalone model.In light of these results, the present study justifies the potential utility of the hybrid MLP-QPSO model to be applied for estimating daily evaporation rates in North of Iran.

The estimation of prediction intervals (PIs) is a major issue limiting the use of Artificial Neural Networks (ANN) solutions for operational streamflow forecasting. Recently, a Lower Upper Bound Estimation (LUBE) method has been proposed that outperforms traditional techniques for ANN-based PI estimation. This method construct ANNs with two output neurons that directly approximate the lower and upper bounds of the PIs. The training is performed by minimizing a coverage width-based criterion (CWC), which is a compound, highly nonlinear and discontinuous function. In this work, we test the suitability of the LUBE approach in producing PIs at different confidence levels (CL) for the 6 hours ahead streamflow discharges of the Susquehanna and Nehalem Rivers, US. Due to the success of Particle Swarm Optimization (PSO) in LUBE applications, variants of this algorithm have been employed for CWC minimization. The results obtained are found to vary substantially depending on the chosen PSO paradigm. While the returned PIs are poor when single-objective swarm optimization is employed, substantial improvements are recorded when a multi-objective framework is considered for ANN development. In particular, the Multi-Objective Fully Informed Particle Swarm (MOFIPS) optimization algorithm is found to return valid PIs for both rivers and for the three CL considered of 90%, 95% and 99%. With average PI widths ranging from a minimum of 7% to a maximum of 15% of the range of the streamflow data in the test datasets, MOFIPS-based LUBE represents a viable option for straightforward design of more reliable interval-based streamflow forecasting models.

 
Keywords: MOFIPS; PSO; prediction interval; rainfall-runoff modeling; LUBE; neural networks; streamflow prediction;

This work proposes an enhanced particle swarm optimization scheme that improves upon the performance of the standard particle swarm optimization algorithm. The proposed algorithm is based on chaos search to solve the problems of stagnation, which is the problem of being trapped in a local optimum and with the risk of premature convergence. Type 1" constriction is incorporated to help strengthen the stability and quality of convergence, and adaptive learning coefficients are utilized to intensify the exploitation and exploration search characteristics of the algorithm. Several well known benchmark functions are operated to verify the effectiveness of the proposed method. The test performance of the proposed method is compared with those of other popular population-based algorithms in the literature. Simulation results clearly demonstrate that the proposed method exhibits faster convergence, escapes local minima, and avoids premature convergence and stagnation in a high-dimensional problem space. The validity of the proposed PSO algorithm is demonstrated using a fuzzy logic-based maximum power point tracking control model for a standalone solar photovoltaic system.

This paper presents a proposed sensorless algorithm for induction motor (IM) speed control based on artificial neural networks (ANNs). The Indirect rotor field oriented (IRFO) technique is applied to control the motor. It is designed based on the proportional integral (PI) controller. The particle swarm optimization (PSO) algorithm is used as a good solution for the problems associated with the design of the proportional integral (PI) controller gains. The PSO is compared with the conventional methods. The proposed controller (PSO-PI) is then integrated with the artificial neural network (ANN) speed estimator. The MATLAB/Simulink is used for the simulation of the system. The obtained simulation results for the proposed technique are very close to the actual ones.

Particle swarm optimization (PSO) is a metaheuristic optimization algorithm that has been used to solve complex optimization problems. The Interior Point Methods (IPMs) are now believed to be the most robust numerical optimization algorithms for solving large-scale nonlinear optimization problems. To overcome the shortcomings of PSO, we proposed the Primal-Dual Asynchronous Particle Swarm Optimization (pdAPSO) algorithm. The Primal Dual provides a better balance between exploration and exploitation, preventing the particles from experiencing premature convergence and been trapped in local minima easily and so producing better results. We compared the performance of pdAPSO with 9 states of the art PSO algorithms using 13 benchmark functions. Our proposed algorithm has very high mean dependability. Also, pdAPSO have a better convergence speed compared to the other 9 algorithms. For instance, on Rosenbrock function, the mean FEs of 8938, 6786, 10,080, 9607, 11,680, 9287, 23,940, 6269 and 6198 are required by PSO-LDIW, CLPSO, pPSA, PSOrank, OLPSO-G, ELPSO, APSO-VI, DNSPSO and MSLPSO respectively to get to the global optima. However, pdAPSO only use 2124 respectively which shows that pdAPSO have the fastest convergence speed. In summary, pdPSO and pdAPSO uses the lowest number of FEs to arrive at acceptable solutions for all the 13 benchmark functions.

One of the best flexible AC transmission system (FACTS) is unified power flow controller (UPFC). As it gets more benefit from both real and reactive power transfer, it is used in power system for controlling the transmitted power. The UPFC controls the power on the transmission side of the power system. When the real as well as reactive power is set the UPFC tries to follow the command by using the proportional and integral (PI) controller. But in some power systems the PI controllers cannot produce the proper power due to the power oscillations. These oscillations are created due to PI controller properties. In this paper the PI controller is replaced with the particle swarm optimization tuned PI controller (PSO-PI). It minimizes the power oscillations by using the objective function. The MATLAB 2017b is used to demonstrate the power transfer curves and the voltages. The IEEE 9 bus system is being used as a reference system.

The goal of this study was to investigate a novel approach of predicting the ultimate capacity of axially loaded circular concrete-filled steel tube (CCFST) columns. A hybrid intelligent system, namely GAP-BART, was developed based on the Bayesian additive regression tree (BART) combining with three nature-inspired optimization algorithms such as Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO), and then applied. Three sub-hybrid models of the system were built, abbreviated as G-BART, A-BART, and P-BART, respectively, using 504 experimental data collected from published research. The compiled database covered five input variables, including the diameter of the circular cross-section-section (D), the wall thickness of the steel tube (t), the length of the column (L), the compressive strength of the concrete (f ′ c), and the yield strength of the steel tube (f y). The coefficient of determination (R 2) values of (0.9971, 0.9982, and 0.9986) and (0.9891, 0.9923 and 0.9931) were achieved for training and testing of G-BART, A-BART, and P-BART models, respectively. The P-BART model performed the lowest RMSE and MAE values for the training and testing set of (66.85 kN and 49.60 kN) and (141.24 kN and 102.04 kN), respectively. These results indicated that although the proposed models were able to estimate ultimate axial capacity with high accuracy, the P-BART model had the best performance among them. For benchmarking, the obtained results were validated against several mathematical approaches as well as other AI techniques (MARS, ANN). The findings of the comparative analysis clearly showed superior ability to predict the CFST ultimate axial capacity relative to the benchmark models. The relative importance of each predictor was investigated to find the most significant input variables. The results confirmed that the hybrid GAP-BART system can serve as a reliable and accurate tool for the design of CCFST columns and to predict their performance.

Reducing the classification process time, for identifying male tissues in medical
images, has always been a challenge. In this paper, an efficient approach is
presented that improves the classification time of breast cancer mammogram
images. First, a preprocessing phase is applied for background correction and
noise removal using Wave Atom transform and a universal cascaded filter,
respectively. Then, the segmentation process is carried out through Particle
Swarm Optimization, Hidden Markov Random Field Model with its Expectation Maximization algorithm, and Kirsch's templates. Finally, features extraction and
classification are employed. The proposed method is used to efficiently reduce
the amount of data that represent the statistically based textural features of the
image. Experimental results show that the employment of the selected
preprocessing algorithms leads to a better classification accuracy than that of the
commonly known digital filters. Moreover, the proposed method improves the
classification time by using only six, out of twenty-two, features that mostly
contains the image information. Finally, classification procedures using Naive Bayes (NB) is applied depending
on the reduced features. The accuracy of the obtained results is compared with literature and good results are
achieved.

In this report the results of the application of genetic algorithms and PSO (Particle Swarm Optimization) are presented to optimize a problem of distributed generation (DG) of power that must meet certain restrictions. For the implementation of the genetic algorithm toolbox of Matlab it is used and implemented fraction varying parameters such as mutation, population etc. This for comparison with the function fmincon already implemented within the Matlab environment and draw conclusions regarding convergence and error rate between data. The PSO algorithm was implemented taking into account stochastic processes based on luck, defining him intrinsic properties, such as population size, inertia factor etc.

In this work, we suggest that the poorer results obtained with Particle Swarm Optimization (PSO) in some previous studies should be attributed to the cross-validation scheme commonly employed to improve generalization of PSO-trained Neural Network River Forecasting (NNRF) models. Cross-validation entails splitting the training dataset into two, and accepting particle position updates only if fitness improvements are concurrently measured on both subsets. The NNRF calibration process thus becomes a multi-objective (MO) optimization problem which is still addressed as a single-objective one. In our opinion, PSO cross-validated training should be carried out under a MO optimization framework instead. Therefore, in this work we introduce a novel MO variant of the swarm optimization algorithm to train NNRF models for the prediction of future streamflow discharges in the Shenandoah River watershed, Virginia (US). The case study comprises over 9,000 observations of both streamflow and rainfall observations, spanning a period of almost 25 years. The newly introduced Multi-objective Fully Informed Particle Swarm (MOFIPS) optimization algorithm is found to provide better performing models with respect to those developed using the standard PSO, as well as advanced gradient-based optimization techniques. These findings encourage the use of a MO approach to NNRF cross-validated training with swarm optimization.

Keywords:
FIPS, multi-objective, particle swarm optimization, PSO, neural network river forecasting, NNRF

This essay aims at reviewing the literature on and discussing two important new theoretical concepts recently proposed for investment analysis and portfolio management in capital markets. The first concept deals with the non-linear nature of actual security returns distribution which not only behaves lognormally but their variance distribution also has a fat tail and high peak, or leptokurtosis. This behavior of security returns contradicts the random walk hypothesis of efficient capital markets which assumes symmetric normality and finite variance. Actual security returns somehow follow other kinds of cross-sectional distributions called fractal distributions whose time-series are characterized by deterministic chaos. The second concept represents the attempts to model non-linearity in price and returns movements through the use of neural networks which are the high-speed artificial intelligence system capable of processing a large amount of market information simultaneously. Not only are these networks able to simulate complicated non-linear relationships among market factors but also learn to mimic the actual market behavior in order to predict the eventual results. Research in this area will enable financial economists to conduct capital market experiments in which their new financial/investment models can be tested without relying on the empirical data, which could be contaminated by undesirable factors.

Inflatable dams are flexible hydraulic structures that are constructed on rivers and are inflated by fluids such as air or water. This research investigates the effects of influential dimensionless factors on estimating one of the critical hydraulic characteristics of inflatable dams, namely the discharge capacity. Various parameters such as the proportion of total upstream head to dam height (H 1 /D h), the ratio of overflowing head to dam height (h/D h), the ratio of discharge per unit width to its maximum value (q/q max), the ratio of the internal pressure of the tube to its maximum value (p/p max) and the ratio of the longitudinal coordinate placement of each element to x max are used. A hybrid model based on the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA), PSO-GA, is proposed to improve the accuracy of the estimation by combining the advantages of both algorithms. Moreover, the performance of the model is compared with available hybrid models, including the Artificial Neural Networks (ANNs) optimized by Stochastic Gradient Descent (SGD) model (ANN-SGD) and the ANN-PSO and ANN-GA models. Finally, the performance of the algorithms is evaluated using statistical indicators such as the coefficient of determination (R 2), root mean square error (RMSE), mean absolute percentage error (MAPE) and the scatter index (SI). The results show that the internal pressure plays a vital role with respect to forecasting the discharge coefficient, and omitting it degrades the accuracy by 2.12%. In comparison with other models, the proposed PSO-GA hybrid model provides the most accurate results (R 2 = 0.999, MAPE = 0.04). Finally, comparing the results of the proposed PSO-GA with the benchmarked ANN-GA, ANN-PSO and ANN-SGD methods proves the superiority of the hybrid PSO-GA method.

In the age of digital and network, every high efficiency and high profit activity has to harmonize with internet. The business behaviors and activities always are the precursor for getting high efficiency and high profit. Consequently, each business behavior and activities have to adjust for integrating with internet. Underlay on the internet, business extension and promotion behaviors and activities general are called the Electronic Commerce (E-commerce). The quality of web-based customer service is the capability of a firm's website to provide individual heed and attention. Today scenario personalization has become a vital business problem in various e-commerce applications, ranging from various dynamic web content presentations. In our paper Iterative technique partitions the customer in terms of frankly combining transactional data of various consumers that forms dissimilar customer behavior for each group, and best customers are acquired, by applying approach such as, IE (Iterative Evolution), ID (Iterative Diminution) and II (Iterative Intermingle) algorithm. The excellence of clustering is improved via Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). In this paper these two algorithms are compared and it is found that Iterative technique chorus Particle Swarm Optimization (PSO) is better than the other Ant Colony Optimization (ACO) algorithms. Additionally the results show that the Particle Swarm Optimization (PSO) algorithm outperforms other Ant Colony Optimization (ACO) algorithms methods. Finally quality is superior along with this response time higher and cost wise performance is increased and both accuracy and efficiency.

This paper presents an application of deploying an adaptive state-space feedback controller for vibration suppression in a flexible cantilever beam system using single and multiple control actuators and deflection sensors optimized with an On-line Particle Swam Optimization (PSO). The PSO is adopted for the optimization problem because of its proved simplicity and performance in different linear and nonlinear applications. Neither expensive computations nor specialized methods are needed. The behavior of the complex nonlinear beam system makes it difficult to locate a global minimum. This nonlinear vibration problem is equivalently changed to the problem of function optimization, which is tackled by using PSO, where a population-based stochastic optimization technique inspired by the social behavior of bird flocking is used, The approach is validated using numerical simulations; and the results confirm the effectiveness of the PSO approach and its ability to highly improve the vibra...