Use Of Particle Swarm Optimization Research Articles

An image encryption approach using particle swarm optimization and chaotic map

Encryption is predominantly crucial in order to provide safeguard to sensitive data, specifically images, against any possible illegitimate access and transgressions. This paper presents to propose an optimized image encryption approach for secure image-based communication. The approach makes use of particle swarm optimization to receive optimized encryption effect and a chaotic map. Initially, the approach generates several encrypted images and chaotic Logistic map, where session key for map’s initial conditions are made dependent on pending plain-image. Subsequently, the encrypted images are served as particles and an initial assemblage to operate optimization through PSO. The optimized encrypted image is manifested by correlation coefficient relevant to contiguous pixels as fitness function. The simulation results of proposed encryption approach demonstrate that encrypted image exhibits high de-correlation of adjacent pixels along with other excellent encryption lineaments such as flat histograms, entropies, net pixel change rates, and unified average changing intensities.

A Dynamical Relay node placement solution for MANETs

Network deployment in wireless networks implies the distribution of the communication nodes to improve some key operational aspects, such as energy saving, coverage, connectivity, or simply reducing the network cost. Most node placement approaches are focused on static scenarios like WSNs, where the topology of the network does not vary over time. Nevertheless, there exist certain situations in which the network node locations can continuously change. In this case, the use of special nodes, so-called Relay Nodes (RNs), contributes to supporting, maintaining or recovering communication in the network. The present work introduces a multi-stage dynamical RN placement solution to lead the RNs to their time-varying optimized positions. The approach, named Dynamical Relay Node placement Solution (DRNS), is based on the use of Particle Swarm Optimization (PSO) algorithms and is inspired by Model Predictive Control (MPC) techniques following a bi-objective optimization procedure, where both network connectivity and throughput are jointly maximized. DRNS is validated in both simulated and real environments composed of mobile robotic nodes, the results showing its goodness and operational suitability for real MANET environments.

Stochastic resonance in an underdamped system with FitzHug-Nagumo potential for weak signal detection

Vibration signals are widely used for bearing fault detection and diagnosis. When signals are acquired in the field, usually, the faulty periodic signal is weak and is concealed by noise. Various de-noising methods have been developed to extract the target signal from the raw signal. Stochastic resonance (SR) is a technique that changed the traditional denoising process, in which the weak periodic fault signal can be identified by adding an expression, the potential, to the raw signal and solving a differential equation problem. However, current SR methods have some deficiencies such us limited filtering performance, low frequency input signal and sequential search for optimum parameters. Consequently, in this study, we explore the application of SR based on the FitzHug-Nagumo (FHN) potential in rolling bearing vibration signals. Besides, we improve the search of the SR optimum parameters by the use of particle swarm optimization (PSO). The effectiveness of the proposed method is verified by using both simulated and real bearing data sets.

A Novel Energy Efficient Clustering Algorithm for Wireless Sensor Networks

Wireless sensor networks (WSNs) are composed of huge amount of tiny resource constrained devices known as sensors. The demand for an energy efficient structure is becoming gradually more essential in WSNs. Energy efficient clustering is a distinguished optimization problem which has been studied extensively to expand the life span of WSNs. In this manuscript, a new energy efficient clustering scheme for WSNs with the use of Particle Swarm Optimization (PSO) is proposed. The clustering algorithm is implemented with the goal of concurrently minimizing the intra-cluster distance along with optimizing the usage of network energy. The presented algorithm is tested widely and results are analyzed and compared with previous ones to show the supremacy in term of alive nodes, energy consumption, packet delivery ratio, and system throughput. Simulation results show that the proposed algorithm outperforms other existing algorithms.

Using Particle Swarm Optimization for Source Seeking in Multi-Agent Systems

This paper presents a novel approach to the source seeking problem, where a group of mobile agents tries to locate the maximum of a scalar field defined on the space in which they are moving. The agents know their position and the local value of the field, and by communicating with their neighbors estimate the gradient direction of the field. A distributed cooperative control scheme is then designed that drives the group towards the maximum while maintaining a specified formation. Previously proposed control schemes that are based on a combination of H∞-optimal formation control and local gradient estimation suffer from premature convergence to local maxima. To overcome this problem, here the use of particle swarm optimization for locating the global maximum is proposed. Agents take the role of particles and an information flow filter approach is employed to separate the consensus dynamics from the local feedback loops governing the agent dynamics. Stability of the overall scheme is established based on the small gain theorem, and by decomposing the synthesis problem for the distributed information flow filter the problem size is reduced to that of a single agent. Simulation results with multiple maxima and quadrocopter models as agents illustrate the practicality of the approach.

Image Watermarking using Singular Value Decomposition with integration of Particle Swarm Optimization in Discrete wavelet transform

This paper proposes an additive watermarking method by using an optimization technique which make use of singular value decomposition (SVD) and makes use of Particle swarm optimization in discrete wavelet domain to enhance the watermarking scheme.SVD based watermarking Is done to provide high capacity. In order to improve the quality of images and to achieve the imperceptibility without losing robustness, an optimal scheme for embedding the secret message derived from PSO. Meanwhile PSO as a popular optimization technique and has been used as a method to balance the imperceptibility and robustness it is because PSO serves the weighting factor inside of embedding process. The proposed technique takes an advantage of Arnold transformations that provide security after successful watermark extraction.DWT is applied on the original image and the value of scaling factors to insert the watermark are obtained using PSO. Result comparisons shows that this algorithm proposed outperforms the additive work as compared to the existing algorithm.

Development of associative memories with transformed data

This study is concerned with the enhancements of performance of associative memories by nonlinear transformations (mappings) of spaces of data to be associated. The data are first transformed into a new space and then the resulting objects (patterns) are stored in the memory. The objective of the transformations is to enhance the recall abilities of the memories. Nonlinear transformations are applied to (i) transform the input space, (ii) transform the output space and (iii) transform both input and output spaces. The transformations are realized in the form of piecewise linear functions whose parameters are optimized with the use of Particle Swarm Optimization (PSO). The optimization is carried out for one-way recall as well as for bi-directional recall. A comprehensive suite of experiments involves several types of associative memories such as correlation associative memories, fuzzy associative memories and morphological associative memories. The experiments reveal some interesting relationships between the parameters of the nonlinear mappings and the resulting quality of the recall. They also help quantify the capabilities of the nonlinear mappings to improve the quality of recall.

Arabic Single-Document Text Summarization Using Particle Swarm Optimization Algorithm

In this research, we propose the use of Particle Swarm Optimization (PSO) algorithm for the extraction of summaries for single Arabic documents. The PSO approach is compared with evolutionary approaches that use Genetic Algorithms (GA) and Harmony Search (HS). The Essex Arabic Summaries Corpus (EASC) and the Recall-Oriented Understanding for Gisting Evaluation (ROUGE) tool are used to evaluate the proposed approach. Experimental results showed that the proposed approach achieved competitive and even higher ROUGE scores in comparison to HS and GA approaches in the state-of-the-art.

S-wave velocity profiling from refraction microtremor Rayleigh wave dispersion curves via PSO inversion algorithm

The refraction microtremor method has been increasingly used as an appealing tool for investigating near surface S-wave structure. However, inversion, as a main stage in processing refraction microtremor data, is challenging for most local search methods due to its high nonlinearity. With the development of data optimization approaches, fast and easier techniques can be employed for processing geophysical data. Recently, particle swarm optimization algorithm has been used in many fields of studies. Use of particle swarm optimization in geophysical inverse problems is a relatively recent development which offers many advantages in dealing with the nonlinearity inherent in such applications. In this study, the reliability and efficiency of particle swarm optimization algorithm in the inversion of refraction microtremor data were investigated. A new framework was also proposed for the inversion of refraction microtremor Rayleigh wave dispersion curves. First, particle swarm optimization code in MATLAB was developed; then, in order to evaluate the efficiency and stability of proposed algorithm, two noise-free and two noise-corrupted synthetic datasets were inverted. Finally, particle swarm optimization inversion algorithm in refraction microtremor data was applied for geotechnical assessment in a case study in the area in city of Tabriz in northwest of Iran. The S-wave structure in the study area successfully delineated. Then, for evaluation, the estimated Vs profile was compared with downhole data available around of the considered area. It could be concluded that particle swarm optimization inversion algorithm is a suitable technique for inverting microtremor waves.

Integrated QoUE and QoS approach for optimal service composition selection in internet of services (IoS)

Service Oriented Architecture (SOA) approaches are presently getting to be appropriate to embedded devices that feature embedded processing and communication. As a result these services get the ability to be hosted on high end machines to wireless resource constrained devices and on any physical object supported with communication ability. This creates the Internet of Services (IoS) environment. Services from multiple owners can be assembled into a composite service irrespective of their specific Quality of Service (QoS) and related properties for implementing a complex business process. In this context service consumer comes against the problem of selecting best service and for which providing QoS relevant guarantees leads to many challenging issues. One among them is determining a feasible service composition that fulfils a set of conditions while maintaining a good Quality of User Experience (QoUE). The last goal suggests the requirement to enforce an extra optimality prerequisite on the feasibility problem. In this paper, an optimization strategy oriented to efficient composite service selection for IoS model is designed through use of Particle Swarm Optimization (PSO) technique. Furthermore, prior to optimization, the services are assured of rich QoUE, especially trustworthiness in terms of reputation. The proposed work evaluates QoUE using the fuzzy based inference algorithm for identifying QoUE satisfied composite service. Experimental evaluation on a set of real world web services demonstrates the effectiveness of our proposed methodology.

Design of Fuzzy Cognitive Maps for Modeling Time Series

This study elaborates on a comprehensive design methodology of fuzzy cognitive maps (FCMs). Here, the maps are regarded as a modeling vehicle of time series. It is apparent that whereas time series are predominantly numeric, FCMs are abstract constructs operating at the level of abstract entities referred to as concepts and represented by the individual nodes of the map. We introduce a mechanism to represent a numeric time series in terms of information granules constructed in the space of amplitude and change of amplitude of the time series, which, in turn, gives rise to a collection of concepts forming the corresponding nodes of the FCMs. Each information granule is mapped onto a node (concept) of the map. We identify two fundamental design phases of FCMs, namely 1) formation of information granules mapping numeric data (time series) into activation levels of information granules (viz., the nodes of the map), and 2) optimization of information granules at the parametric level, viz., learning (estimating) the weights between the nodes of the map. The learning is typically realized in a supervised mode on a basis of some experimental data. A construction of information granules is realized with the aid of fuzzy clustering, namely fuzzy C-means. The optimization is realized with the use of particle swarm optimization. The proposed approach is illustrated in detail by a series of experiments using a collection of publicly available data.

Wind turbine system optimisation using interval T2FL tuned with PSO

Wind turbines were developed in response to the increasing demand for renewable energy sources. They are being used all around the world for energy production and in order to maximise this production in all-weather conditions algorithms for maximum power point tracking have been developed, the most widely used being Perturb and Observe. Fuzzy logic was introduced due to the inconveniences of this method, to further the development and have better handling of uncertainty the type 2 fuzzy logic is applied. It has been proven many times to be efficient and robust. And in order to adapt and tune it the use of particle swarm optimisation is combined. This work will focus on the tracking of the MPP (maximum power point) using the IT2FL combined with PSO.

Adaptive Adjustment factor in Image Enhancement Technique using Particle Swarm Optimization with Aging Leader and Challengers (ALC-PSO)

The process of converting an image and performing some operations on it is known as image processing. The application of Image processing include: Image Enhancement. Image enhancement is a process of transforming an image and enhancing its quality in order to take out some important information from it. The image enhancement process can be done by an optimization technique like Particle Swarm Optimization method, which is a search technique used to find the best solutions out of several possible ones. The PSO technique can be efficiently used by taking the objective function for image enhancement as the fitness value for every particle image. This paper presents an overview of image enhancement techniques based on the use of Particle Swarm Optimization. The limitation of static adjustment factor is removed by using Particle Swarm Optimization with Aging Leader and Challengers (ALC-PSO). A new algorithm of image enhancement using ALC-PSO has been proposed, which will adaptively find the adjustment factor for the efficient performance of the process of image enhancement.

Optimization of the rotor geometry of the line-start permanent magnet synchronous motor by the use of particle swarm optimization

Purpose – The purpose of this paper is to elaborate the methodology and software for the optimization of rotor structure of the line-start permanent magnet synchronous motor (LSPMSM). To prove usefulness of presented approach the case study problem has been solved. Design/methodology/approach – The modified particle swarm optimization (PSO) algorithm has been employed for the optimization of LSPMSM. The optimization solver has been elaborated in Delphi environment. The software consists of two modules: an optimization solver and a numerical model of LSPMSM. The model of the considered machine has been developed in the ANSYS Maxwell environment. In the optimization procedure the objective function has been based on maximizing efficiency and power factor. Findings – Obtained results show that modified PSO algorithm can be successfully applied for the optimization of the rotor structure of LSPMSM. This software can be used as a design tool to improve the performance of LSPMSM. The results of studied case pro...

PSO-based Optimal PID Controller for Twin Rotor MIMO System

Optimum design methods based on soft computing have proved their excellence in industrial automatic control systems. In this paper, a proposed system is presented to treat the tuning of proportional, integral and derivative (PID) controller parameters to control the behavior of the Twin Rotor Multi-Input-Multi-Output (MIMO) System (TRMS). The system can be decoupled into two paths the main and the tail. The use of Particle Swarm Optimization (PSO) algorithm for tuning the controller parameters aims to get a stable control system. The simulation results reveal significant improvements in the step response characteristic such as reducing the steady state error, rise time, settling time and the number of overshoots in position control of the TRMS. Thus, the PSO based PID controller in this system proves better performance compared to the traditional existing PID one.

Three-way decisions based on decision-theoretic rough sets under linguistic assessment with the aid of group decision making

Based on decision-theoretic rough set model of three-way decisions, we augment the existing model by introducing linguistic terms. Considering the two types of parameters being used in the three-way decisions with linguistic assessment, a certain type of novel three-way decisions based on the Bayesian decision procedure is constructed. In this way, three-way decisions with decision-theoretic rough sets are extended to the qualitative environment. With the aid of multi-attribute group decision making, the values of these parameters are determined. An adaptive algorithm supporting consistency improvement of multi-attribute group decision making is designed. Then, we optimize the scales of the linguistic terms with the use of particle swarm optimization. The values of these parameters of three-way decisions are aggregated when proceeding with group decision making. Finally, the proposed model of three-way decisions with linguistic assessment is applied to the selection process of new product ideas.

Designing of Elastoplastic Adaptive Truss Structures with the Use of Particle Swarm Optimization

In the paper we demonstrate how Particle Swarm Optimization (PSO) can be employed to solve the Adaptive Impact Absorption (AIA) problem. We consider a truss structure which is subjected to impact loads. Stiff bars can be replaced by elastoplastic fuses which control theirs dynamical response. The point of optimization is to maximize or minimize a given objective function by redesigning the structure. This is realized by redistributing the initial mass, finding proper fuse localizations and adjusting, in real-time, the elastoplastic limits. Comparing to the previous results, we show that PSO is capable of achieving results at least as good as gradient-based optimization, having at the same time much larger flexibility regarding the definition of the objective function. This gives significantly broader field of potential applications. In particular, we present how PSO can be used to solve the simultaneous optimization problem: mass redistribution and fuse positioning for a set of expected, various impacts.

Induction Motor Control Using PSO-ANFIS

Abstract The speed of the Induction motor can be adjusted to a great extent so as to provide easy control and high performance. In this paper an effort is made to develop a prototype model to control the speed of an induction motor using PSO-ANFIS hybrid technique. We describe the use of Particle Swarm Optimization (PSO) and ANFIS for designing an optimal fuzzy logic controller of an Induction Motor. We have used two input parameters like speed, torque and output is firing angle. PSO-ANFIS speed controller obtains better dynamic behavior and superior performance of the Induction motor speed control. Similar approach can be correlated to the control of plasma column and which can be implemented in fusion reactor to control the plasma column. Plasma position and shape control is very crucial for the overall performance of the fusion reactor such as tokamak. The quality of the discharge in the Saskatchewan TORus-Modified (STOR-M) tokamak is strongly related to the position of the plasma column within the discharge vessel. If the plasma column approaches too near the wall, then either minor or complete disruption occurs. Consequently it is necessary to be able to control dynamically the position of the plasma column throughout the entire discharge. A comparison analysis of PSO-ANFIS and Fuzzy Back Propagation algorithm is taken in our work to control the speed of induction motor, where the PSO-ANFIS gives better result in terms of fast computing.

Application of a combined particle swarm optimization and perturb and observe method for MPPT in PV systems under partial shading conditions

This paper explains the development of a new algorithm for maximum power point tracking (MPPT) in large PV systems under partial shading conditions (PSC). The new algorithm combines the use of particle swarm optimization (PSO) for MPPT during the initial stages of tracking and then employs the traditional perturb and observe (PO) method at the final stages. The methodology has been first simulated in two different PV configurations under varying shading patterns and experimentally verified using a microcontroller based experimental system. The integration of swarm intelligence with PO algorithm is shown to yield faster convergence to the global maximum power point (GMPP) than when the two methods are individually used. The oscillations in the output power, voltage and current of the PV system with the proposed method are the least when compared to the ones obtained during PSO based MPPT.

Reliability Modeling on Time between Failures of NC Machine Tools

A reliability modeling method is proposed based on the time between failures of NC machine tools. According to the field data of the time between failures of a certain type of NC machine tools, parameter estimation of reliability model is made by use of particle swarm optimization (PSO)-based correlation coefficient optimization method, and it is proved that time between failure of CNC machine tool obeyed mathematical model of 3-parameter Weibull distribution via hypothesis testing. This provides the basis for reliability assessment of NC machine tool as well as the development of maintenance strategy in the future. Meanwhile, it also provides a theoretical reference for the reliability prediction of the whole NC machine tool.