Alignment of two optical fiber based on particle swarm optimization technique

In this paper, we present a comprehensive and in-depth investigation on the optimization of Proportional-Integral (PI) controller tuning for achieving stability and desired overshoot in the step response. The main objective of this study is to compare the effectiveness of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques in finding the optimal parameters for the PI controller. The PI controller is a widely used control algorithm that plays a crucial role in many industrial processes. Its tuning greatly affects the system's performance, particularly in terms of stability and overshoot. Therefore, finding the optimal tuning parameters is of utmost importance. To address this optimization problem, we propose the utilization of two popular metaheuristic algorithms, GA and PSO. These algorithms are known for their ability to efficiently search through large solution spaces and find near-optimal solutions. By applying these algorithms to the PI controller tuning problem, we aim to determine which technique yields better results in terms of stability and overshoot tuning. In our comparative study, we provide a detailed explanation of both GA and PSO algorithms, focusing on their working principles and mathematical formulations. We also describe how these algorithms can be applied to the PI controller tuning problem. Furthermore, we highlight the key differences between GA and PSO, shedding light on their strengths and weaknesses. To assess the performance of GA and PSO, we conduct several experiments using different benchmark functions and step response models. We measure the stability and overshoot metrics for various parameter settings obtained through GA and PSO. By thoroughly analyzing the obtained results, we draw meaningful conclusions regarding the effectiveness of each technique. Our findings demonstrate that both GA and PSO exhibit promising results in optimizing PI controller tuning. These observations provide valuable insights and guidelines for choosing the appropriate algorithm based on specific control requirements. In conclusion, this comparative study is thought to contribute to the field of control systems engineering by offering a comprehensive analysis of GA and PSO techniques in the context of PI controller tuning. By highlighting their strengths and weaknesses, it is aimed to provide researchers and practitioners with valuable information for making informed decisions when optimizing control parameters for stability and overshoot reduction purposes.

In the modern world with the advancement of technologies in solid-state electronics, the use of power electronics devices is playing a crucial role in the conversion and control of electric power. By using these innovative devices in 3-phase voltage source inverters, precise and smooth control over the conversion of electrical power can be achieved. But on the other hand, this leads to Total Harmonic Distortion (THD) leading to an adverse effect on the overall system performance. The optimization technique has emerged as a newly popular method for selective harmonic elimination. The main focus of this paper is on simulation study for harmonic analysis of 3-phase voltage source inverter by using particle swarm optimization (PSO) technique. By considering the expression of THD as an objective function, optimization is achieved in the PSO algorithm. This paper also discusses the harmonic analysis of the proposed inverter for conventional PWM techniques such as sinusoidal pulse width modulation (SPWM) and trapezoidal pulse width modulation (TPWM). The harmonic spectrum of the output voltage of the inverter is compared for SPWM, TPWM, and PSO by using MATLAB. From THD analysis, it is observed that PSO gives better performance than other conventional techniques.

This paper presents performance comparative study of various particle swarm optimization (PSO) techniques for the placement of generator units in the distributed generation (DG) system. For the installation of generator units in the distributed generation system, it is very important to know the generator sizing and its placement in the network system for reducing the line losses and hence the cost. Various PSO techniques such as Canonical PSO, Hierarchical PSO (HPSO), Time varying acceleration coefficient (TVAC) PSO, Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC), Stochastic inertia weight (Sto-IW) PSO and Time varying inertia weight (TVIW) PSO have been used for comparative study. Here the main objective function (OF) is to minimize the system cost. These techniques have been tested on the standard IEEE-14 bus, IEEE-30 bus and IEEE-57 bus network system by the use of MATLAB software.

We have developed a powerful method for crystal structure prediction from "scratch" through particle swarm optimization (PSO) algorithm within the evolutionary scheme. PSO technique is dramatically different with the genetic algorithm and has apparently avoided the use of evolution operators (e.g., crossover and mutation). The approach is based on a highly efficient global minimization of free energy surfaces merging total-energy calculations via PSO technique and requires only chemical compositions for a given compound to predict stable or metastable structures at given external conditions (e.g., pressure). A particularly devised geometrical structure factor method which allows the elimination of similar structures during structure evolution was implemented to enhance the structure search efficiency. The application of designed variable unit cell size technique has greatly reduced the computational cost. Moreover, the symmetry constraint imposed in the structure generation enables the realization of diverse structures, leads to significantly reduced search space and optimization variables, and thus fastens the global structural convergence. The PSO algorithm has been successfully applied to the prediction of many known systems (e.g., elemental, binary and ternary compounds) with various chemical bonding environments (e.g., metallic, ionic, and covalent bonding). The remarkable success rate demonstrates the reliability of this methodology and illustrates the great promise of PSO as a major technique on crystal structure determination.

PurposeThis paper aims to employ particle swarm optimization (PSO) technique for optimum design of single‐phase induction motor (SPIM) on the basis of maximizing the efficiency of the motor simultaneously satisfying a set of performance constraints.Design/methodology/approachThe design problem of a SPIM is presented as a nonlinear optimization problem on the basis of maximizing the efficiency of the motor. A set of performance constraints are imposed in the optimization procedure. Particle swarm optimization technique is used as an optimization tool for obtaining the motor dimensions corresponding to maximum efficiency. Incorporation of PSO as a derivative free optimization technique in solving SPIM optimum design problem significantly relieves the assumptions imposed on the optimized objective function.FindingsThis approach has been applied to two sample motors and the results are compared with the evolutionary programming (EP) results. It is observed that the proposed approach is effective and robust.Originality/valueThe fields of application of SPIMs are expanding day by day. The emphasis on energy conservation demands an improvement of the efficiency of single‐phase induction motors. Hence, the design optimization of these motors with efficiency as objective function assumes great importance.

There has been ongoing experiments on a two-wheeled self-balancing robot for several years, and it is almost similar to the model of an inverted pendulum on a cart. The presented paper describes the model, design, working and simulation of a two-wheeled self-balancing robot. Properties of the system under study are extremely non-linear. Moreover, there is a great deal of instability in the controller of proposed system. To improve the stability of mobile robots, this paper introduces fractional order PID (FOPID) or PI <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">λ</sup> D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">μ</sup> controller for a non-linear and high-order system and compares it with the conventional PID. FOPID controller is designed specifically to adjust the parameters of the system more accurately according to desired specifications. The factorrs of the considered controller are optimized using both Nelder-Mead (N-M) technique and Particle Swarm Optimization (PSO) technique. The results obtained by N-M technique are compared with those of PSO technique. N-M tuned FOPID controller significantly improves the quality of controller in contrast to PSO technique. Simulation results also indicate that Nelder-Mead tuned FOPID is a better and more stable controller as compared to classical PID or PSO tuned FOPID, for a non-linear system like a two-wheeled self-balancing robot.

Investigating a hybrid of Tone-Model and Particle Swarm Optimization techniques in transcribing polyphonic guitar sound

With rapid advancement in technologies used in solid-state electronics, nowadays, the conversion and control of electrical power has become easy due to inclusion of power electronics-based devices. With the help of these devices, accurate and smooth control of electrical power conversion can be consummated in three-phase voltage source inverters. But uses of power electronics devices also result in Total Harmonic Distortion (THD), which distorts overall system performance. Eliminating some selected harmonic in a system can quickly be done by using optimization techniques. This manuscript focuses on analysis of harmonics in a 3-phase voltage source inverter using Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) technique.

Segmentation is an important step in iris recognition system because the accuracy of the iris recognition system is affected by the segmentation of the iris. In this paper, an efficient method has been proposed for the segmentation of non-ideal iris images captured under uncooperative conditions. A fuzzy c-means clustering algorithm based on Particle Swarm Optimization (PSO) technique has been employed as a pre-segmentation step in the iris recognition framework. The fuzzy c-means clustering method delimits the iris and eliminates the unwanted portions of an image. The particle swarm optimization technique is incorporated to avoid FCM fall into local minimum. The segmentation accuracy of the proposed method is implemented by considering CASIA v3 Interval and UBIRIS databases. The proposed method is compared with the classical segmentation methods and has an encouraging performance.

Glioma detection based on multi-fractal features of segmented brain MRI by particle swarm optimization techniques

In this paper, an algorithm for order reduction of linear multivariable systems is proposed using the combined advantages of the dominant pole retention method and the error minimisation by Particle Swarm Optimisation (PSO) technique. The denominator of the reduced order transfer function matrix is obtained by retaining the dominant poles of the original system while the numerator terms of the lower order transfer matrix are determined by minimising the Integral Square Error (ISE) in between the transient responses of original and reduced order models using PSO technique. The proposed algorithm guarantees stability of the reduced order transfer function matrix if the original High Order System (HOS) is stable and is having superior features, including easy implementation, stable convergence characteristic and good computational efficiency. The proposed algorithm has been applied successfully to the transfer function matrix of a 10th order two-input two-output linear time invariant model of a practical power system. The performance of the algorithm is tested by comparing the relevant computer simulation results.

Particle Swarm Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO – evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations the geometrical place consists more than one single point. The performance of the newly proposed PSO algorithm is compared with evolutionary algorithms. The main advantage of the PSO technique is its speed of convergence. Also, we propose a hybrid algorithm, combining PSO and evolutionary algorithms. The hybrid combination is able to detect the geometrical place very fast for which the evolutionary algorithms required more time and the conventional PSO approach even failed to find the real geometrical place.

ABSTRACTThis study proposes an energy absorption model for predicting the effect of loading rates, concrete compressive strength, shear span-to-depth ratio, and longitudinal and transverse reinforcement ratio of reinforced concrete (RC) beams using the particle swarm optimization (PSO) technique. This technique avoids the exhaustive traditional trial-and-error procedure for obtaining the coefficient of the proposed model. Fifty-six RC slender and deep beams are collected from the literature and used to build the proposed model. Three performance measures, namely, mean absolute error, mean absolute percentage error and root mean square error, are investigated in the proposed model to increase its accuracy. The design procedure and accuracy of the proposed model are illustrated and analysed via simulation tests in a MATLAB/Simulink environment. The results indicate the minimal effect of swarm size on the convergence of the PSO algorithm, as well as the ability of PSO to search for an optimum set of coefficients from within the solution space.

In this paper, a novel approach of hybrid controller made up of Proportional-Integral-Derivative (PID) controller and Support Vector Machine (SVM) is proposed, for the navigation of mobile robot. In proposed hybrid technique, the two parameters of mobile robot i.e. velocity and turning angles are controlled by PID controller and support vector machine respectively. Particle Swarm Optimization (PSO) technique is used to find the global optimum values of PID parameters within the search space. PSO technique helps to achieve, stable convergence characteristics and fast tuning of the process. The proposed hybrid approach enables mobile robot to track path accurately with computational efficiency. Robustness and accuracy of the proposed hybrid model is illustrated through simulation results for circular and random path.

&lt;span&gt;In this paper, modified multilevel inverter, via addition of an auxiliary bidirectional switch, based on Newton Raphson (NR) and Particle Swarm Optimization (PSO) techniques is presented. The NR and PSO techniques were employed for selective harmonics elimination (SHE) solution in a modified Cascaded H Bridge Multilevel inverter (CHB-MLI). The Selective Harmonic Elimination Pulse-Width Modulation (SHE-PWM) is a powerful technique for harmonic minimization in multilevel inverter. The NR and PSO techniques were used to determine the switching angles by solving the non-linear equations of the output voltage waveform of the modified CHB-MLI in order to control the fundamental component and eliminate some low order harmonics. The proposed NR and PSO techniques are capable to minimize the Total Harmonic Distortion (THD) of the output voltage of the modified inverter within allowable limits. This paper aims to modeling and simulation by MATLAB of the modified topology of the CHB-MLI for a single-phase prototype for 13-levels. The inverter offers less THD and greater efficiency using PSO control algorithm compared with the NR algorithm. &lt;br https://server9.kproxy.com/servlet/redirect.srv/sruj/snbzofspy/skvyzff/p1/&gt; The performance of the proposed controllers based on NR and PSO techniques is verified through simulation.&lt;/span&gt;