Particle Swarm Optimization Technique Research Articles

Economic Dispatch for Grid-Connected Wind Power With Battery-Supercapacitor Hybrid Energy Storage System

This study demonstrates an effective dispatching scheme of utility-scale wind power at one-hour increments for an entire day with a hybrid energy storage system consisting of a battery and a supercapacitor (SC). Accurate forecasting of wind power is crucial for generation scheduling and economic operation. Here, wind speed is predicted by one hour ahead of time using a multilayer perceptron Artificial Neural Network, which exhibits satisfactory performance with good convergence mapping between input and target output data. Furthermore, an adaptive neuro-fuzzy inference system is employed to devise a state of charge (SOC) controller to accurately estimate the grid reference power (P <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_\mathrm{Grid,ref}$</tex-math></inline-formula> ) for each one-hour dispatching period. This type of desired P <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_\mathrm{Grid,ref}$</tex-math></inline-formula> estimation is critical to ensure the energy storage system (ESS) completes each dispatching period with its starting SOC and has adequate capacity available for next-day operation. Also, the particle swarm optimization technique is implemented to optimize the life cycle cost of the ESS based on its depth of discharge usage, which is vital for minimizing the cost of a dispatchable wind power scheme. The actual wind speed data of four different days as a representative of each season recorded at Oak Ridge National Laboratory are utilized in the simulations to provide a realistic economic assessment for dispatching the wind power.

Hybrid model for classification of diseases using data mining and particle swarm optimisation techniques

Hybrid model for classification of diseases using data mining and particle swarm optimisation techniques

Bi-level energy management system for optimal real time operation of grid tied multi-nanogrids

• Proposing an efficient on-line EMS considering uncertainties of weather conditions, load demand and electricity price during real time. • Introducing an efficient day-ahead scheduling of interconnected nano-grids using an improved version of Aquila Optimization (IAO) algorithm. • Presenting demand side management to enhance the efficiency of the proposed EMS. • Exploiting the cooperation between the nano-grids for economic operation. Integrating distributed energy resources (DERs) utilizing renewable energy sources enhances the performance and efficiency of the power system. Nano-grid (NG) comprises a small scale DERs at a small building. This paper presents an efficient bi-level energy management system (EMS), day-ahead and real time scheduling, for economic operation of grid tied multi-interconnected NGs. Balancing between generation and consumption is secured through exchanging power with the utility grid so the daily energy consumption cost could be optimized. The proposed day-ahead scheduling is applied through two stages. The first is demand side management (DSM) while the second is determining the optimal set-points of different sources in NGs. The optimal set-points obtained from day-ahead scheduling are rescheduled and updated during real time to keep the economic operation under uncertainties of weather conditions, grid tariff and load demand. Advanced meta-heuristic techniques can handle with energy scheduling optimization problem, complex non-linear optimization problem, efficiently considering the different operation constraints. Comparative performance study of the original Aquila optimizer (AO), improved aquila optimizer (IAO), and particle swarm optimization technique (PSO) is investigated in this paper. Results obtained demonstrate that the interconnection between NGs with an IAO based bi-level EMS provides the optimal economic operation.

Hybrid model for classification of diseases using data mining and particle swarm optimisation techniques

Hybrid model for classification of diseases using data mining and particle swarm optimisation techniques

Application of bagging and particle swarm optimisation techniques to predict technology sector stock prices in the era of the COVID-19 pandemic using the support vector regression method

The increase in positive cases of COVID-19 not only affects the health and lifestyle, but also the economy and the stock market. Tech and digital sector stocks can be predicted to be one of the most profitable. Therefore, the prediction of the stock price is required to be able to forecast the prospects of investment in the future. In this study, the prediction of the stock prices of Multipolar Technologies Ltd. (MLPT) was carried out using the support vector regression (SVR) method with bootstrap aggregation (bagging) technique and particle swarm optimisation (PSO) as SVR optimisation. From the results of the prediction process, it is shown that the application of bagging and PSO techniques in predicting stock prices in the technological sector can reduce the root mean squared error (RMSE) value on the SVR, the RMSE value from 22.142 to 21.833. Although it does not have a big impact, it is better to apply a combination of bagging and PSO techniques to SVR than one of them (SVR/SVR - PSO/SVR-Bagging).

Parametric study of FSW process on AA6061-Activated carbon composite using particle swarm optimization

Now a days concerning about a metal matrix composite is welded by Friction Stir welding (FSW) techniques and also FSW parameter study method is optimised Particle Swarm optimisation (PSO) technique. The developed material was developed by modified down pouring stir casting technique with matrix as aluminium alloy and reinforcement as activated carbon. Using the response surface methodology of composite is assessed by mechanical and morphological evaluations with their regression expressions. The experimental process is planned on developed composite with connection parameters like spindle speed, axial load, tool pin, and materials combination. In experiment the standard of weld is indebted by tensile study and hardness of the composite. The developed optimization model is matured to optimize the four contribution input levels of welding process. From optimization results confirmative check is executed and results shows less error from experimental values.

Design Methodology for Wireless Backhaul/Fronthaul Using Free Space Optics and Fibers

The recent increase in data rates for Free space optics (FSO) transmission technology, means they could be used for designing the backhaul/fronthaul for 5G and beyond cellular networks. The flexibility and cost-effectiveness provided by FSO are the primary reasons for the mobile operators to investigate the potential of the technology as a mobile backhaul/fronthaul. Unfortunately, the reliability of FSO links is weather dependent, especially if the link covers considerable distance. Optical fibers, on the other hand, are expensive but more reliable. Hence, optimally designing a hybrid network consisting of both fiber and FSO connections can bring in the cost-effectiveness of FSO as well as the robustness of fibers. In such a design, the more important links are connected using fibers while the links with higher tolerance towards failure are designed using FSO. Therefore, in this paper, we propose a hybrid FSO/fiber backhaul/fronthauling methodology for connecting wireless base stations (BSs) to the network core. We first formulate a mixed integer non-linear program (MINLP) for determining the number of splitter/FSO distribution points required in the network that optimally provide connectivity to the BSs. The MINLP is designed to identify the locations of the splitters/FSO distribution points as well. Thereafter, we solve the MINLP with the help of particle swarm optimisation (PSO) and mixed integer linear programming (MILP) techniques. We also derive a heuristic for solving the MILP. Finally, we propose another method for determining the number of splitters required in a relatively shorter time: K-means cluster based method. The results verify that the hybrid network is cost-effective while conforming to the data rate and reliability requirements of the links. The proposal allows evaluation of a seamless design solution of the hybrid network with practical time-complexity.

CDIPEM: Cardiovascular disease incidence probability estimation CNN model

Heart disease affects the majority of the world’s population, with a high rate of morbidity and mortality. The prediction of heart disease at an early stage is viewed as one of the imperative issues in clinical trials. Since there is a huge amount of medical data and it keeps on increasing, it becomes very difficult to analyze and process such data. Hence, machine learning algorithms become an obvious choice to handle such data. This paper presents a classification framework for clinical decision-making that uses an optimized convolutional neural network (CNN). The pre-processed clinical dataset is used for the training and testing of the classifier. A publicly available UCI dataset is used to examine how the system performs. The statistical features are extracted at the beginning and then subsequent data minimization is carried out. Therein-after, the prediction of heart diseases is done with the help of CNN. The identification of optimal weights is done to enhance the performance of CNN which in turn gives accurate disease prediction results. In this paper, we propose a hybrid Particle Swarm Optimization (PSO) and Gray wolf Optimization (GWO) technique namely PS-GW technique to identify the optimal weight parameters. Finally, performance analysis is done and the experimental results are compared with the existing approaches which show improved classification accuracy over conventional optimization techniques and some well-known classifiers such as k-nearest neighbour, decision tree, logistic regression, naive bayes and random forest.

An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

A Mobile Ad hoc Network (MANET) is a group of low-power consumption of wireless mobile nodes that configure a wireless network without the assistance of any existing infrastructure/centralized organization. The primary aim of MANETs is to extend flexibility into the self-directed, mobile, and wireless domain, in which a cluster of autonomous nodes forms a MANET routing system. An Intrusion Detection System (IDS) is a tool that examines a network for malicious behavior/policy violations. A network monitoring system is often used to report/gather any suspicious attacks/violations. An IDS is a software program or hardware system that monitors network/security traffic for malicious attacks, sending out alerts whenever it detects malicious nodes. The impact of Dynamic Source Routing (DSR) in MANETs challenging blackhole attack is investigated in this research article. The Cluster Trust Adaptive Acknowledgement (CTAA) method is used to identify unauthorised and malfunctioning nodes in a MANET environment. MANET system is active and provides successful delivery of a data packet, which implements Kalman Filters (KF) to anticipate node trustworthiness. Furthermore, KF is used to eliminate synchronisation errors that arise during the sending and receiving data. In order to provide an energy-efficient solution and to minimize network traffic, route optimization in MANET by using Multi-Objective Particle Swarm Optimization (MOPSO) technique to determine the optimal number of clustered MANET along with energy dissipation in nodes. According to the research findings, the proposed CTAA-MPSO achieves a Packet Delivery Ratio (PDR) of 3.3%. In MANET, the PDR of CTAA-MPSO improves CTAA-PSO by 3.5% at 30% malware.

A Novel Framework for DDoS Attacks Detection Using Hybrid LSTM Techniques

The recent development of cloud computing offers various services on demand for organization and individual users, such as storage, shared computing space, networking, etc. Although Cloud Computing provides various advantages for users, it remains vulnerable to many types of attacks that attract cyber criminals. Distributed Denial of Service (DDoS) is the most common type of attack on cloud computing. Consequently, Cloud computing professionals and security experts have focused on the growth of preventive processes towards DDoS attacks. Since DDoS attacks have become increasingly widespread, it becomes difficult for some DDoS attack methods based on individual network flow features to distinguish various types of DDoS attacks. Further, the monitoring pattern of traffic changes and accurate detection of DDoS attacks are most important and urgent. In this research work, DDoS attack detection methods based on deep belief network feature extraction and Hybrid Long Short-Term Memory (LSTM) model have been proposed with NSL-KDD dataset. In Hybrid LSTM method, the Particle Swarm Optimization (PSO) technique, which is combined to optimize the weights of the LSTM neural network, reduces the prediction error. This deep belief network method is used to extract the features of IP packets, and it identifies DDoS attacks based on PSO-LSTM model. Moreover, it accurately predicts normal network traffic and detects anomalies resulting from DDoS attacks. The proposed PSO-LSTM architecture outperforms the classification techniques including standard Support Vector Machine (SVM) and LSTM in terms of attack detection performance along with the results of the measurement of accuracy, recall, f-measure, precision.

A Hybrid K-Means and Particle Swarm Optimization Technique for Solving the Rechargeable E-Scooters Problem

A Hybrid K-Means and Particle Swarm Optimization Technique for Solving the Rechargeable E-Scooters Problem

A production inventory model for high-tech products involving two production runs and a product variation

&lt;p style='text-indent:20px;'&gt;This paper explores a production inventory model considering two high-tech products of the same kind. One is the primary product and the other is the updated version of that primary product. Due to continuous development in technology, the life-cycle of some high-tech products, like, smartphone, tablet, laptop, etc. have become shorter. We witness the launching of new products more frequently in this field. This prompts the manufacturers to release an updated or pro version of their existing products after a certain time to compete in the market. The reputation of the primary product (in terms of quality and performance) plays an important role in generating the demand for the updated product. Due to the short life-cycle of the products, the proposed model considers only two consecutive production runs. One for the primary product and one for the updated product. Here the demands of both the products depend on the respective selling prices. Moreover, the demand of the updated product is also dependent on the quality of the primary product. Shortages for the primary product are allowed. Those shortages are backlogged partially with the updated product. Also, the possibility of imperfect production during regular production runs is considered. The selling prices, production rates, and the production run times for both the products are considered here as decision variables. Due to the complexity in the resulting optimization problem, the quantum-behaved particle swarm optimization technique is applied to derive the optimal profit. The concavity natures of the profit function are shown graphically. A numerical illustration is presented for the economic validation of the model. Finally, sensitivity analysis of the optimal solutions concerning the key inventory parameters is conducted for identifying several managerial implications.&lt;/p&gt;

Retracted: A Systematic Literature Review on Particle Swarm Optimization Techniques for Medical Diseases Detection.

[This retracts the article DOI: 10.1155/2021/5990999.].

Pricing strategy in an interval-valued production inventory model for high-tech products under demand disruption and price revision

Due to continuous development in technology, new and updated products are launching in the market more frequently in the area of some high-tech products such as smartphones, laptops, etc. It is noticed that after a certain period of releasing a new product by a particular company some other company develops a similar type of product at a lesser selling price. Customers generally become attracted to buy that updated product causing a sudden disruption in the demand for the first product. The demand for a normal product may also suddenly vanish as we have experienced during the COVID-19 lockdown period. The manufacturer is then compelled to reduce the selling price to sell the remaining products. This paper aims at developing a single period production inventory model addressing this particular market condition. This paper also considers carbon emissions from different inventory processes and examines the optimal inventory policies under the cap and trade regulatory policy. Again, in a real-life production system, the various inventory cost components and the carbon emission rates from different inventory processes are not fixed always. To incorporate this issue, the proposed model considers these quantities as interval numbers. The resulting optimization problem is thus also interval-valued and has been solved by using the quantum-behaved particle swarm optimization technique. A numerical illustration is provided to validate the proposed model. Finally, a sensitivity analysis with respect to key inventory parameters is performed to derive some key managerial implications. It is found that the frequency of launching new products is inversely proportional to the optimum profit of the manufacturer. Also, a higher carbon tax rate is found to be beneficial from an environmental point of view.

Designing Meyer wavelet neural networks for the three-species food chain model

&lt;abstract&gt; &lt;p&gt;The current research work is related to present the numerical solutions of three-species food chain model (TS-FCM) by exploiting the strength of Meyer wavelet neural networks (MWNNs) along with the global and local search competencies. The particle swarm optimization technique works as a global operator, while the sequential quadratic programming scheme is applied as a local operator for the TS-FCM. The nonlinear TS-FCM is dependent upon three categories, called consistent of prey populations, specialist predator and top predator. The optimization of an error-based fitness function is presented by using the hybrid computing efficiency of the global and local search schemes, which is designed through the differential form of the designed ordinary differential model and its initial conditions. The proposed results of the TS-FCM are calculated through the stochastic numerical techniques and further comparison is performed by the Adams method to check the exactness of the scheme. The absolute error in good ranges is performed, which shows the competency of the proposed solver. Moreover, different statistical procedures have also been used to check the reliability of the proposed stochastic procedure along with forty numbers of independent trials and 10 numbers of neurons.&lt;/p&gt; &lt;/abstract&gt;

OQFL: An Optimized Quantum-Based Federated Learning Framework for Defending Against Adversarial Attacks in Intelligent Transportation Systems

Intelligent transportation systems, especially Autonomous Vehicles (AVs), are emerging as a paradigm with the potential to change modern society. However, with this, there is a strong need to ensure the security and privacy of such systems. AV ecosystems depend on machine learning algorithms to autonomously control their operations. Given the amount of personal information AVs collect, coupled with the distributed nature of such ecosystems, there is a movement to employ federated learning algorithms to develop secure decision-making models. Although federated learning is a viable candidate for data privacy, it is vulnerable to adversarial attacks, particularly data poisoning attacks, where malicious vectors would be injected in the training phase. Additionally, hyperparameters play an important role in establishing an efficient federated learning model that can be resilient against adversarial attacks. In this paper, to address these challenges, we propose a novel Optimized Quantum-based Federated Learning (OQFL) framework to automatically adjust the hyperparameters of federated learning using various adversarial attacks in AV settings. This work is innovative in two ways: first, a quantum-behaved particle swarm optimization technique is used to update the hyperparameters of the learning rate, local and global epochs. Second, the proposed technique is utilized within a cyber defense framework to defend against adversarial attacks. The performance of the proposed framework was evaluated using two benchmark datasets: MINST and Fashion-MINST, where they include images that would be extracted from smart cameras of AVs. This framework is shown to be more resilient against various adversarial attacks compared with peer techniques.

Progressive Transfer Learning-based Deep Q Network for DDOS Defence in WSN

In The Wireless Multimedia Sensor Network (WNSMs) have achieved popularity among diverse communities as a result of technological breakthroughs in sensor and current gadgets. By utilising portable technologies, it achieves solid and significant results in wireless communication, media transfer, and digital transmission. Sensor nodes have been used in agriculture and industry to detect characteristics such as temperature, moisture content, and other environmental conditions in recent decades. WNSMs have also made apps easier to use by giving devices self-governing access to send and process data connected with appropriate audio and video information. Many video sensor network studies focus on lowering power consumption and increasing transmission capacity, but the main demand is data reliability. Because of the obstacles in the sensor nodes, WMSN is subjected to a variety of attacks, including Denial of Service (DoS) attacks. Deep Convolutional Neural Network is designed with the state-action relationship mapping which is used to identify the DDOS Attackers present in the Wireless Sensor Networks for Smart Agriculture. The Proposed work it performs the data collection about the traffic conditions and identifies the deviation between the network conditions such as packet loss due to network congestion and the presence of attackers in the network. It reduces the attacker detection delay and improves the detection accuracy. In order to protect the network against DoS assaults, an improved machine learning technique must be offered. An efficient Deep Neural Network approach is provided for detecting DoS in WMSN. The required parameters are selected using an adaptive particle swarm optimization technique. The ratio of packet transmission, energy consumption, latency, network length, and throughput will be used to evaluate the approach’s efficiency.

Improved Model for Genetic Algorithm-Based Accurate Lung Cancer Segmentation and Classification

Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients. For lung cancer diagnosis, the computed tomography (CT) scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis. In present scenario of medical data processing, the cancer detection process is very time consuming and exactitude. For that, this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm. In the model, the input CT images are pre-processed with the filters called adaptive median filter and average filter. The filtered images are enhanced with histogram equalization and the ROI(Regions of Interest) cancer tissues are segmented using Guaranteed Convergence Particle Swarm Optimization technique. For classification of images, Probabilistic Neural Networks (PNN) based classification is used. The experimentation is carried out by simulating the model in MATLAB, with the input CT lung images LIDC-IDRI (Lung Image Database Consortium-Image Database Resource Initiative) benchmark Dataset. The results ensure that the proposed model outperforms existing methods with accurate classification results with minimal processing time.

Particle Swarm Optimization for Solving Sine-Gordan Equation

The term ‘optimization’ refers to the process of maximizing the beneficial attributes of a mathematical function or system while minimizing the unfavorable ones. The majority of real-world situations can be modelled as an optimization problem. The complex nature of models restricts traditional optimization techniques to obtain a global optimal solution and paves the path for global optimization methods. Particle Swarm Optimization is a potential global optimization technique that has been widely used to address problems in a variety of fields. The idea of this research is to use exponential basis functions and the particle swarm optimization technique to find a numerical solution for the Sine-Gordan equation, whose numerical solutions show the soliton form and has diverse applications. The implemented optimization technique is employed to determine the involved parameter in the basis functions, which was previously approximated as a random number in the work reported till now in the literature. The obtained results are comparable with the results obtained in the literature. The work is presented in the form of figures and tables and is found encouraging.

Retracted: Optimization of Correlation Filters Using Extended Particle Swarm Optimization Technique.

[This retracts the article DOI: 10.1155/2021/6321860.].