Modified Particle Swarm Optimization Approach Research Articles

Performance Optimization of High-Altitude Platform Wireless Communication Network Exploiting TVWS Spectrums Based on Modified PSO

The Uniform hexagonal array (UHA) sidelobe level (SLL) reduction is a crucial consideration for avoiding co-channel interference and improving Carrier-to-interference Ratio (CIR) performance in HAP wireless networks using the TVWS spectrum. The SLL reduction in UHA has been researched by applying evolutionary algorithms such as genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, such algorithms have the drawbacks such as early convergence and the inability to reach globally. The beam pointing approaches for UHA antennas using Modified PSO (MPSO), for reducing the SLL on finding the optimal current weights is presented in this article. The UHA antenna array with 19 cells fitted with 169 elements in the HAPs cellular structure is considered to simulate the proposed approach. Then, the MPSO approach is employed and compared with the standard PSO algorithm and Uniform Weighting (UW) scheme. As per the simulation findings, the proposed approach drastically decreased sidelobes, with a reduction of up to -6.84dB and -13.3dB when compared to PSO and UW respectively. In other words, the proposed approach outperforms the UW and the PSO algorithms. Moreover, the CIR performance of the proposed approach has been demonstrated in terms of average outage probability and the proposed approach provides a better result.

Effective Resource Aware Health Care Monitoring in Body Sensor Network Platform Using Modified Particle Swarm Optimization

Real-time physiological data may be gathered using wearable medical sensors based on a network of body sensors. We do not however have an effective, trustworthy and secure body sensor network platform (BSN) that can satisfy growing e-health requirements. Many of these applications require BSN to provide the dependable and energy efficient data transfer of many data speeds. Cloud computing is giving assets to patient dependent on application request at SLA (service level agreement) rules. The service providers are focusing on giving the necessity based asset to satisfy the QoS (quality of service) prerequisites. Therefore, it has become an assessment to adapt service-oriented assets because of vulnerability and active interest for cloud services. The task scheduling is an option in contrast to appropriating asset by evaluating the inconsistent outstanding task at hand. the allocation of tasks given by the microprocessor Subsequently, a productive asset scheduling method needs to disseminate proper VMs (Virtual Machines). The swarm intelligence is appropriate to deal with such vulnerability issues carefully. In this paper, an effective resource scheduling strategy Utilizing Modified Particle Swarm Optimization approach (MPSO) is presented, with a target to limit execution cost that gives an approach for the microprocessor to deal with the multiple number of tasks gives to the controllers in order to perform the multiple tasks that gets logged in the cloud via Internet of things technology (Iot), energy consumed, bandwidth consumption, speed and execution cost. The near investigation of results has been exhibited that the presented scheduling scheme performed better when contrasted with existing evaluation. In this manner, the presented resource scheduling approach might be utilized to enhance the viability of cloud resources.

Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot

Existing studies for the balance control of unmanned bicycle robots only consider constant forward velocity and a single optimal objective that cannot be applied to the complex motion situation. To balance the bicycle robot with time-varying forward velocity, only with the steering actuator, the multiple objective optimal balance control issue is studied here. A fuzzy state-space model under different forward velocities is firstly offered based on the non-linear Euler–Lagrange model. Based on this, a closed-loop equation under a fuzzy controller is verified. To regulate the feedback gain of the fuzzy controller, a modified particle swarm optimization (MPSO) algorithm with two stages is proposed. In the MPSO's second stage, a novel objective fitness function, consisting of multiple objectives and combining the conventional Hurwitz stability analysis criterium, is designed. Procedures for the MPSO dynamic programming approach are presented. By two examples, the efficiency of the MPSO algorithm, for time-varying and time-constant velocity situations, and faster capacity for iteration convergence, are examined.

Feature Selection for Alzheimer’s Gene Expression Data Using Modified Binary Particle Swarm Optimization

Alzheimer’s Disease (AD) is a neurological disorder that destroys memory and other significant mental functions. One of the most accurate methods to identify the disease-causing genes is to monitor gene expression values in various samples. Selecting significant genes for classification is important in gene expression studies. In this study, the experimental data are taken from the gene expression data of human brain in persons with AD and older control subjects GEO GSE5281 data set. In this work, a new two-step gene selection is applied to filter the noisy and redundant genes, based on the statistical method and heuristic optimization approach. T-statistic (T-test), Signal to Noise Ratio (SNR) and F-test, are used in the first step of the gene selection process. The top ten significant genes selected from the statistical methods are applied to Particle Swarm Optimization (PSO) to obtain the optimal number of features of Alzheimer’s disease. To avoid the stagnation issue in PSO, a modified PSO approach is proposed which finds a new particle position by utilizing the Genetic Algorithm (GA) crossover and mutation operators. The classifiers, Decision tree, Support Vector Machine (SVM), Linear Model, Random Forest and Neural network, are employed in training and testing data to analyse the performance of GA & PSOs. Modified PSO with t-Test in Random forest and Linear model provides 100% accuracy for the test dataset of GSE5281 with optimum number of genes. The significant genes identified through this research are EGR1, CKMT1B, RPL15, PSMB3, GRK4, COX6A1 and PHIP from the GSE5281 dataset.

Prediction Accuracy Model Aiming to Improve Prediction Accuracy in Congenital Heart Anomaly Detection using Hybrid Feature Selection with Modified Particle Swarm Optimization Approach

Heart Disease is one of the primary causes of mortality and morbidity in the Globe since the 19th Century. Most of the Globalized Multi-Specialty Hospitals are not able to control and governed by emerging technologies, at the same time, the death rate escalates day by day in addition to Covid-19 is a multifaceted state. Heart disease classification involves identifying numerous health problems and sickness symptoms of ones’ individual with significant feature selection, there is misclassification probability that could be very high and priceless. There are many diverse methods were designing for heart disease prediction systems in earlier days, even though it is unsolved and rising the death rate. As observed by many research groups, PSO is an intensive computational and inspired biologically inspired algorithms like Genetic Algorithms (GA) has a proven track record to handle computationally complex problems with competence for predicting heart diseases. This research contribution through the proposed model downs the computation time and increases the accuracy. The high-level comprehensibility, predictive accuracy are good and desired through this Intelligence Hybrid Approach (IHP) to reduce Heart attacks and control the death rate.

A Modified Particle Swarm Optimization Approach for Latency of Wireless Sensor Networks

In time-sensitive applications, such as detecting environmental and individual nuclear radiation exposure, wireless sensor networks are employed.. Such application requires timely detection of radiation levels so that appropriate emergency measures are applied to protect people and the environment from radiation hazards. In these networks, collision and interference in communication between sensor nodes cause more end-to-end delay and reduce the network's performance. A time-division multiple-access (TDMA) media access control protocol guarantees minimum latency and low power consumption. It also overcomes the problem of interference. TDMA scheduling problem determines the minimum length conflict-free assignment of slots in a TDMA frame where each node or link is activated at least once. This paper proposes a meta-heuristic centralized contention-free approach based on TDMA, a modified particle swarm optimization. This approach realizes the TDMA scheduling more efficiently compared with other existing algorithms. Extensive simulations were performed to evaluate the modified approach. The simulation results prove that the proposed scheduling algorithm has a better performance in wireless sensor networks than the interference degree leaves order algorithm and interference degree remaining leaves order algorithm. The results demonstrate also that integrating the proposed algorithm in TDMA protocols significantly optimizes the communication latency reduction and increases the network reliability.

Modelling material flow using the Milk run and Kanban systems in the automotive industry

AbstractMaterial flow management refers to the analysis and specific optimization of the inventory‐production system. Material flow can be characterized as the organized flow of material in a production process with the required sequence determined by a technological procedure. The Milk run system assures the transportation of materials at the right time and in an optimal manner. It should be combined with the Kanban system to highlight when something is required in the production process. This paper presents biological swarm intelligence, in general, and a particular model, particle swarm optimization (PSO), for modelling material flow using a Milk run system supported by a Kanban system in the automotive industry. The aim of this study is to create a new model for the optimal number of trailers for one train and optimal number of containers in a tugger train system when the route time period has been defined. A new modified PSO approach for integrating inventory‐production in a unique optimization model is used. The major modification to the original PSO is using the capacity of a container instead of a velocity component. Each new Kanban trigger is checked, and the total timing for the Milk run delivery solution is calculated for the necessary raw material capacity for each shop floor.

Particle Swarm Optimization for the Fusion of Thermal and Visible Descriptors in Face Recognition Systems

In this paper, we propose a novel face recognition system based on fusing thermal and visible descriptors. The proposed approach is divided in two steps: training and validation. In the training stage, the system obtained the optimal weights from the particle swarm optimization (PSO) algorithm to maximize the recognition rates obtained from different combinations of local descriptor methods using a standard thermal face database (Equinox database). The weights were then used to fuse visible and thermal face descriptors to achieve high recognition rates during the validation stage using the Pontificia Universidad Católica de Valparaiso-Visible Thermal Face (PUCV-VTF) database. Three local matching methods were used to perform the face recognition: local binary pattern, histograms of the oriented gradients, and local derivative pattern. In addition, this paper considers a comparison with the following methods: a previous work based on Genetic Algorithms and a modified PSO approach. The results of this paper show recognition rates over 99% for the PUCV-VTF database, largely surpassing the results for Genetic Algorithms. The fusion methodology is found to be unaffected to variations in illumination and expression conditions, combining the visible and thermal information efficiently through the PSO algorithm, and thus choosing the optimal regions where a given spectrum is more relevant.

Optimal design of water distribution networks using simple modified particle swarm optimization approach

Optimal design of water distribution networks using simple modified particle swarm optimization approach

Simultaneous coordination of distinct plug-in Hybrid Electric Vehicle charging stations: A modified Particle Swarm Optimization approach

As Plug-in Hybrid Electric Vehicles are environment friendly, it is envisioned as an alternative solution for the existing transportation system. However, simultaneous charging of electric vehicles may stimulate increase in peak load, causing voltage deviation and power loss issues. Different from other approaches, this paper presents an alternative way to tackle these issues. A novel two area distribution system, each following a different mobility pattern is presented. This model considers various sites namely optimal, midst, and unfit site for locating the charging stations in both the areas simultaneously. Moreover, the scheduling of the plug-in Hybrid Electric Vehicle is addressed using a meta-heuristic solving tool as it involves simultaneous scheduling of vehicles in both areas. To illustrate the cost reduction obtained with this model, the performance is evaluated in a standard test system namely IEEE 69-bus radial distribution system. The test results support the efficiency of the proposed two area framework and the new solving tool. The economic and the stability aspect of the system are addressed under various scenarios and several important findings are revealed.

Swarm intelligence based on modified PSO algorithm for the optimization of axial-flow pump impeller

This paper presents a multi-objective optimization of the impeller shape of an axial-flow pump based on the Modified particle swarm optimization (MPSO) algorithm. At first, an impeller shape was designed and used as a reference in the optimization process then NPSHr and η of the axial flow pump were numerically investigated by using the commercial software ANSYS with the design variables concerning hub angle βh, chord angle βc, cascade solidity of chord σc and maximum thickness of blade H. By using the Group method of data handling (GMDH) type neural networks in commercial software DTREG, the corresponding polynomial representation for NPSHr and η with respect to the design variables were obtained. A benchmark test was employed to evaluate the performance of the MPSO algorithm in comparison with other particle swarm algorithms. Later the MPSO approach was used for Pareto based optimization. Finally, the MPSO optimization result and CFD simulation result were compared in a re-evaluation process. By using swarm intelligence based on the modified PSO algorithm, better performance pump with higher efficiency and lower NPSHr could be obtained. This novel algorithm was successfully applied for the optimization of axial-flow pump impeller shape design.

Simulation‐based crack growth mitigation through optimum laser peened residual stress

PurposeThe purpose of this paper is to develop an approach to optimize the cycles‐to‐failure of a peened component with respect to laser peening (LP) variables: pressure magnitude, mid‐span, and spot size when the component is subject to a variable amplitude loading.Design/methodology/approachTo optimally design an LP process, an experimentally validated 3D finite element simulation of the LP process, a cycles‐to‐failure estimation capability incorporating residual stress, and a particle swarm optimization strategy were developed and employed to maximize the cycles‐to‐failure of a component of a titanium turbine disk.FindingsThe most critical finding of this research is that a minor difference in the residual stress profile can lead to a large difference in the cycles‐to‐failure. This finding implies that selecting the optimization objective to be the cycles‐to‐failure is a better option as compared to the residual stress profile.Research limitations/implicationsThe LP‐induced residual stresses are assumed static and do not change as number of load cycles increase.Originality/valueThe paper develops a framework that relates the LP variables and the cycles‐to‐failure of a peened component. A modified particle swarm optimization approach is developed to optimize the fatigue life of a turbine disk.

Reuse water network synthesis by modified PSO approach

In this work, reuse water networks have been modeled and optimized by the application of the Particle Swarm Optimization (PSO) method. From a built-in superstructure, which represents the whole possible configurations of the reuse water networks, a modified PSO method was proposed in order to lead with both discrete- and continuous-type variables in Non-Linear Programming (NLP) and Mixed Integer Non-Linear Programming (MINLP) models that represent water allocation problems. By introducing pinch analysis concepts, the improved PSO method has shown excellent performance in the optimality of reuse water network synthesis based on the minimization of freshwater flow rate, number of interconnections and annual total cost. PSO results were compared with those of the General Algebric Modeling System (GAMS) for all criteria, showing the flexibility and efficiency of the metaheuristic approach in solving water network reuse synthesis problems.

Model order formulation of a multivariable discrete system using a modified particle swarm optimization approach

This paper proposes an algorithm for model order formulation of an absolutely stable higher order linear time invariant multivariable discrete system using a new version of evolutionary computing technique namely, Modified Particle Swarm Optimization (MPSO). A simple adjunct polynomial method has been proposed for obtaining the initial seed values of the lower order multivariable system. In the modified PSO, the movement of a particle is governed by three behaviors namely, inertia, cognitive and social. The cognitive behavior helps the particle to remember its previously visited best position. This paper proposes to split the cognitive behavior into two sections. This modification is efficiently utilized to obtain a better lower order system that reflects the characteristics of the original higher order system by minimizing the integral squared error with the steady state constraints. The results obtained are compared with the earlier techniques utilized, to validate its ease of computation. The proposed algorithm is illustrated with a numerical example from the literature.