Journal of Intelligent & Fuzzy Systems - Volume 38, issue 5

Authors: Ramadas, Rithvik | Chowdhury, Anirban

Article Type: Research Article

Abstract: Voice User Interfaces have become popular with the advent of Alexa, Google Home, Cortana and other commercial speech recognition interfaces; however, the privacy of the end users is compromised while using these interfaces in public. In addition, users can feel a bit awkward while using these interfaces with loud voice while they are outside their homes. Contextually, ‘Hypnosis’ and ‘Hypnotherapy’ have not been frequently applied as a way of human communications although the principle of suggestion induced behavior changes in an interesting approach to interact with machines. In this paper, GEORGIE a prototype AI was used to achieve a novel …means of interaction inspired from the principles of hypnotherapy, which is a discrete interface ensuring that end-users’ privacy is not compromised. It is envisaged that people who prefer secret communication and interaction might love to use this hypnotic computer interface (hypCI). The hypCI would be the novel means of human robot interface (HRI) or human computer interface (HCI). Show more

Keywords: Artificial intelligence, cognitive science, defense, hypnotic triggers, user experience design, voice user interfaces

DOI: 10.3233/JIFS-179731

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6507-6516, 2020

Authors: Murali, Ritwik | Shunmuga Velayutham, C.

Article Type: Research Article

Abstract:  This paper attempts to employ Evolutionary Algorithm(EA) techniques to evolve variants of a computer virus(Timid ) that successfully evades popular antivirus scanners. Generating authentic variants of a specific malware results in a valid database of malware variants, which is sought by anti-malware scanners, so as to identify the variants before they are released by malware developers. This preliminary investigation applies EAs to mutate the Timid virus with a simple code evasion strategy, i.e., insertion and deletion(if available) of a specific assembly code instruction directly into the virus source code. Starting with a database of over 60 popular antivirus scanners, …this EA based approach for malware variant generation successfully evolves Timid variants that evade more than 97% of the antivirus scanners. The results from these preliminary investigations demonstrate the potential for EA based malware generation and also opens up avenues for further analysis. Show more

Keywords: Anti-malware research, cyber security, evolutionary algorithms, malware, malware creation, virus

DOI: 10.3233/JIFS-179732

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6517-6526, 2020

Authors: Patil, Nilesh Vishwasrao | Rama Krishna, C. | Kumar, Krishan

Article Type: Research Article

Abstract:  A Distributed Denial of Service (DDoS) attack is the biggest threat to Internet-based applications and consumes victim service by sending a massive amount of attack traffic. In the literature, numerous approaches are available to protect the victim from the DDoS attacks. However, the attack incidents are increasing year by year. Further, several issues exist in the traditional framework based detection system such as itself becoming a victim, slow detection, no real-time response, etc. Therefore, the traditional framework based system is not capable of processing live traffic in the big data environment. This paper proposes a novel Spark streaming-based distributed and …real-time DDoS detection system called S-DDoS. The proposed S-DDoS system employs the K-Means clustering algorithm to recognize the DDoS attack traffic in real-time. The proposed detection model designed on the Apache Hadoop framework using highly scalable H2O sparkling water. The detection model deployed on the Spark framework to classify live traffic flows. The results show that the proposed S-DDoS detection system efficiently detects the DDoS attack from network traffic flows with higher detection accuracy (98% ). Show more

Keywords: Distributed denial of service (DDoS), K-means clustering algorithm, big data, entropy, network security, apache spark

DOI: 10.3233/JIFS-179733

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6527-6535, 2020

Authors: Dickson, Anne | Thomas, Ciza

Article Type: Research Article

Abstract:  Intrusion detection system is a second layer of defence in a secured network environment. When comes to an IoT platform, the role of IDS is very critical since it is highly vulnerable to security threats. For a trustworthy intrusion detection system in a network, it is necessary to improve the true positives with minimum false positives. Research reveals that the true positive and false positive are conflicting objectives that are to be simultaneously optimized and hence their trade-off always exists as a major challenge. This paper presents a method to solve the tradeoff among these conflicting objectives using multi-objective particle …swarm optimization approach. We conducted empirical analysis of the system with multiple machine learning classifiers. Experimental results reveals that this technique with J48 classifier gives the highest gbest value 10.77 with minimum optimum value of false positive 0.02 and maximum true positive 0.995. Empirical evaluation shows an incredible improvement in Pareto set in the objective function space by attaining an optimum point. Show more

Keywords: Intrusion detection system, receiver operating characteristics, particle swarm optimization, pareto front, multi-objective optimization, non-linear programming

DOI: 10.3233/JIFS-179734

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6537-6547, 2020

Authors: Hussain, Muzakkir | Saad Alam, Mohammad | Sufyan Beg, M.M. | Akhtar, Nadeem

Article Type: Research Article

Abstract:  Vehicular Fog Computing (VFC) is a natural extension of Fog Computing (FC) in Intelligent Transportation Systems (ITS). It is an emerging computing model that leverages latency aware and energy aware application deployment in ITS. However, due to heterogeneity, scale and dynamicity of vehicular networks (VN), deployment of VFC is a challenging task. In this paper, we propose a multi-objective optimization model towards minimizing the response time and energy consumption of VFC applications. Using the concepts of probability and queuing theory, we propose an efficient offloading scheme for the fog computing nodes (FCN) used in VFC architecture. The optimization model is …then solved using a modified differential evolution (MDE) algorithm. Extensive experimentations performed on real-world vehicular trace of Shenzhen, reveals the superiority of proposed VFC framework over generic cloud platforms. Show more

Keywords: Intelligent transportation systems, modified differential evolution, vehicular fog computing

DOI: 10.3233/JIFS-179735

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6549-6560, 2020

Authors: Chandrawanshi, Veervrat Singh | Tripathi, Rajiv Kumar | Pachauri, Rahul

Article Type: Research Article

Abstract:  The design of a wireless sensor network (WSN) faces many constraints. Mostly, WSN is energy constraint because the sensor nodes are battery operated. Available power expenditure in WSN largely depends on the efficient use of limited resources and appropriate routing of the data packets. The power consumption can be minimized by balancing the energy consumption between the sensor nodes and selecting the minimum power consumption route for the data packets. Clustering is one of the most effective technique that not only uniformly distributes the energy among all the sensor nodes but also play a vital role in the designing of …routing protocols. So based on these advantages, a low power consumption routing protocol is proposed that makes use of fuzzy c-means++ algorithm. The proposed approach minimizes the power consumption of the sensor network by the excellent management of the WSN and also raises the lifespan. The simulation result illustrates the effectiveness of the proposed routing method when compared with the recently developed protocols based on k-means and fuzzy c-means algorithms. Show more

Keywords: Wireless sensor networks, clustering, cluster head, k-means, k-means++, fuzzy c-means algorithm, fuzzy c-means++ algorithm, energy efficient network

DOI: 10.3233/JIFS-179736

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6561-6570, 2020

Authors: Suriya Praba, T. | Sethukarasi, T. | Venkatesh, Veeramuthu

Article Type: Research Article

Abstract: In wireless sensor networks (WSN), the establishment of large-scale sensor networks has always needed attention. One of the many challenges is to set up an architecture that is different from the rest and find mechanisms that can efficiently scale up with the growing number of nodes that may be essential to ensure sufficient coverage of large areas under study. Concurrently, these new architectures and mechanisms are supposed to maintain low power consumption per node to comply with energy guaranty acceptable network lifetime. The researchers utilized numerous Data collection techniques for the prompt data aggregation, yet still those outcomes the node …with path failures. To solve this issue, the mobile sink is being extensively used for data aggregation in large scale wireless sensor networks (WSNs). This technique avoids imbalances in energy consumption due to multi-hop transmission but might lead to extended delay time. In this paper, our focus is on shortening the length of the mobile sink’s travelling path to reduce the delay time during data gathering in large scale WSN. To achieve this, the mobile sink visits the cluster heads in an optimized path instead of sensors one by one. Here Hierarchical clusters are efficiently formed by modified K- means with outlier elimination and node proximity and residual energy based second level clustering algorithm. Next, we determine the optimal path for the mobile sink by formulating KH based Travelling Salesman Problem solving optimization algorithm. This technique proposed reduces not only the length of the path travelled by the mobile sink but also lessens the computational effort that is required for travelling-path planning and enhances the lifetime of nodes. And to ensure aggregation accuracy in cluster heads iterative filtering is implemented. Our experimental results show the proposed algorithm shortens the tour length by 40–60 percent compared to Bacterial foraging optimization-based TSP algorithm. Also delivers better results compared to other’s in terms of the computational effort, time, energy use, and enhances the network lifetime. Show more

Keywords: Wireless sensor networks, data aggregation, clustering, travelling salesman problem, krill herd optimization

DOI: 10.3233/JIFS-179737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6571-6581, 2020

Authors: Mathi, Senthilkumar | Joseph, Eric | Advaith, M.S. | Gopikrishna, K.S. | Gopakumar, Rohit

Article Type: Research Article

Abstract: The rapid increase in internet usage for the past few decades has steered to higher demand and for adequate support for the network mobility in heterogeneous networks. However, in the existing mobile IPv6 (Internet Protocol version 6) protocols such as traditional, hierarchical, proxy and related methodologies have been stated to manage the recurrent mobility of the devices in a network with the centralized feature. However, the single point of failure, route optimization, handoff latency and security threats are highly exposed when the number of mobile device increases in the centralized approach. Also, it leads to the limitation of the size …of binding information when the mobile host needs to update its place. Hence, this paper suggests a secure and optimized architecture by distributing mobility functions as distributed access points. Also, the paper addresses the prevention measures for security attacks such as false binding message, rerun and hijack. The proposed scheme is simulated and validated using network simulator and security model verifier – AVISPA. Finally, the numerical and experimental outcomes demonstrate that the proposed scheme offers a substantial diminution in the cost of the binding update, binding refresh, and packet delivery. Show more

Keywords: IP networks, distributed access point, mobile agents, routing, integrity, authentication

DOI: 10.3233/JIFS-179738

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6583-6593, 2020

Authors: Sujatha, M. | Geetha, K. | Balakrishnan, P. | Renugadevi, T.

Article Type: Research Article

Abstract: The unprecedented growth in personal, business and research data motivates users to lease storage from multiple cloud storage providers like Amazon, Azure, etc. Selection of cost-effective cloud storage service by considering different pricing policies along with their performance characteristics is a challenging task. This research proposes a model named as OUTFIT (Optimal sUgeno Type Fuzzy Inference sysTem) an optimal data storage hosting model by suggesting an appropriate storage type based on user demands. In the first phase, we have surveyed Amazon, Google Cloud, Azure and Rackspace cloud storage providers and consolidated the different cloud storage types supported by them. In …the second phase the cloud service providers are ranked by using Sugeno fuzzy inference system based on the user preference. The third phase designates the appropriate service that incurs minimal estimated storage usage cost. The proposed approach is able to categorize various cloud service providers with an optimal grading process by including multiple decision criteria for fine-grained storage type selection. The observed results prove it to be a more favourable selection tool in comparison with its counterpart tools like Cloudorado, RightCloudz in terms of cost. Show more

Keywords: CSP selection, QoS Attributes, Sugeno Inference system, Iaas storage selection, Cloud computing

DOI: 10.3233/JIFS-179739

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6595-6605, 2020

Authors: Pradeep Kumar, K.A. | Thiruvengadathan, Rajagopalan | Shanmugha Sundaram, G.A.

Article Type: Research Article

Abstract:  The perfect Y antenna array configuration is among the most prevalent antenna array arrangement used in radio interferometry for synthesis imaging. It is crucial to determine an antenna array configuration that could offer further higher quality radio-images. In this paper, a novel and an efficient L-band log-periodic spiral antenna array design is presented. The radio-imaging performance of the log-periodic spiral antenna array is compared and shown to outperform an equivalent perfect Y antenna array. Radio imaging performance is evaluated using the computational simulation for the proposed L-band log-periodic spiral antenna array and the equivalent perfect Y antenna array. The metric …used for evaluation is the Structural Similarity Index (SSIM) and Surface Brightness Sensitivity (SBS). The L-band log-periodic spiral antenna array was observed to have about five times higher bandwidth, 2.24 times greater sensitivity, angular resolution better by a factor of five, and 10% wider field of view than the perfect Y configuration antenna array of comparable extent. It has been analytically demonstrated that the log-periodic spiral antenna array is an optimum configuration based on Chow’ s optimization technique. The L-band log-periodic spiral antenna array has outperformed the perfect Y configuration in many different imaging aspects. Show more

Keywords: Radio telescope, interferometry, synthesis array, log-periodic antenna array, antenna array configuration

DOI: 10.3233/JIFS-179740

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6607-6618, 2020

Authors: Zhilenkov, Anton A. | Chernyi, Sergei G. | Sokolov, Sergei S. | Nyrkov, Anatoliy P.

Article Type: Research Article

Abstract: The safe and reliable navigation of such autonomous systems as unmanned aerial vehicles (UAV) is a complex open problem in robotics, where a robotic system must simultaneously do many tasks of perception, control and localization. This task is especially complicated when working in an uncontrolled, unpredictable environment, for example, on city streets, in wooded areas, etc. In these cases, the autonomous agent must not only be guided to avoid collisions, but also interact safely with other agents in the environment. The developed system allows navigation of unmanned aerial vehicles in difficult environmental conditions. The results of training and the operation …of the autonomous navigation system in the forest are presented. The system finds and follows the paths that are fairly difficult to distinguish. The results of field experiments are presented. Presentation of the model is presented on the youtube.com channel. Show more

Keywords: Localization, agent, robotic, control

DOI: 10.3233/JIFS-179741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6619-6625, 2020

Authors: Mor, Navdeep | Sood, Hemant | Goyal, Tripta

Article Type: Research Article

Abstract: Over the last few years, road accidents in developing countries are increasing at an alarming rate. In India, almost 3% of GDP is getting wasted in road accidents, which not only cause social problems but, also, imposes a huge burden on the Indian economy. Various researches have been done to analyze this situation using different methods and techniques on different stretches and intersections. This paper makes one of the first attempts to develop an Accident Prediction Model (APM) in the Indian State of Haryana. This study describes the procedure for collection and analysis of accident data, as well as the …detailed methodology used to develop APMs. The Models were developed using one of the most common algorithms of machine learning i.e. linear regression technique. Results obtained from APM of Haryana State were compared with the results given by some of the highly successful APMs like Smeed’s Model, Valli’s Model and their comparisons were discussed to find the most efficient model. It was observed that the proposed model shows highly accurate results in predicting road accidents in Haryana. The output of this work can be used for theoretical as well as practical applications like road safety management for improving existing conditions of the road network in Haryana and to regulate new traffic safety policies in the future. Show more

Keywords: Accident prediction model, linear regression, road safety, accidents

DOI: 10.3233/JIFS-179742

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6627-6636, 2020

Authors: Shukla, Alok Kumar | Pippal, Sanjeev Kumar | Gupta, Srishti | Ramachandra Reddy, B. | Tripathi, Diwakar

Article Type: Research Article

Abstract: Feature selection is a pre-processing method that identifies the significant features from high-dimensional data and able to diminish the computational cost of the learning algorithm because of removing the irrelevant and redundant features. It has traditionally been applied in a wide range of problems that include biological data processing, pattern recognition, and computer vision. The aim of this paper is to identify the best feature subsets from the benchmark datasets which improve the performance of the classifiers. Existing filter-based feature selection approaches fail to choose the relevant features from the original feature sets. To obtain the tiny subset of relevant …features, we have introduced a novel filter-based feature selection method, called ReCFS. The proposed method is a combination of both feature-feature correlation and nearest neighbor weighted features to find an optimal subset of features to minimize correlation among features. The effectiveness of the selected feature subset by proposed method is evaluated by using two classifiers such as Naïve Bayes and K-Nearest Neighbour on real-life datasets. For the diverse performance measurements, the experiments are conducted on eight real-life datasets of varied dimensionality and number of instances. The result demonstrates that the proposed method has found promising feature subsets which improved the classification accuracy over competing feature selection methods Show more

Keywords: Machine learning, relief-F, correlation feature selection, classification, naïve bayes

DOI: 10.3233/JIFS-179743

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6637-6648, 2020

Authors: Pradhan, Rosy | Majhi, Santosh Kumar | Jaypuria, Jemarani

Article Type: Research Article

Abstract: Moth-Flame optimization is a meta-heuristic algorithm based on the navigation behaviour of moths. Generally, moth’s poses a very effective mechanism called transverse orientation while moving a long distance in night and maintain of fixed angle with respect to the moon. MFO suffers with local optima and stagnation problem, in order to improve the performance and exploration rate of the existing algorithm and for solving the complex real world problems, a new version of MFO algorithms is proposed by adding the concept of orthogonality feature. The modified algorithm is termed as orthogonal Moth-Flame optimization (OMFO) algorithm. The main objective of this …OMFO is going to solve the convergence problem to minimization of the search space and avoid the local optima. The proposed method can also be used to maintain the balance between exploration and exploitation. In this work, a set of 28 standard IEEE CEC 2017 benchmark test functions with 10 and 30 dimensions are used to evaluate and compare between the obtained results which prove that the proposed OMFO gives very promising and competitive performance as well as achieve better performance over original MFO algorithm with high stability over searching method. The efficiency of the proposed method is verified by applying in model order reduction problem. The performed analysis such as statistical measure, convergence analysis and complexity measure reveal that the proposed method is reliable and efficient in solving practical optimization problems. Show more

Keywords: Meta-heuristic, optimization, MFO, orthogonality, model order reduction

DOI: 10.3233/JIFS-179744

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6649-6661, 2020

Authors: Jayachitra Devi, Salam | Singh, Buddha

Article Type: Research Article

Abstract: Link prediction tremendously gained interest in the field of machine learning and data mining due to its real world applicability on various fields such as in social network analysis, biomedicine, e-commerce, scientific community, etc. Several link prediction methods have been developed which mainly focuses on the topological features of the network structure, to figure out the link prediction problem. Here, the main aim of this paper is to perform feature extraction from the given real time complex network using subgraph extraction technique and labeling of the vertices in the subgraph according to the distance from the vertex associated with each …target link. This proposed model helps to learn the topological pattern from the extracted subgraph without using the topological properties of each vertex. The Geodesic distance measure is used in labeling of the vertices in the subgraph. The feature extraction is carried out with different size of the subgraph as K  = 10and K  = 15. Then the features are fit to different machine learning classification model. For the evaluation purpose, area under the ROC curve (AUC) metric is used. Further, comparative analysis of the existing link prediction methods is performed to have a clear picture of their variability in the performance of each network. Later, the experimental results obtained from different machine learning classifiers based on AUC metric have been presented. From the analysis, we can conclude that AdaBoost, Adaptive Logistic Regression, Bagging and Random forest maintain great performance comparatively on all the network. Finally, comparative analysis has been carried out between some best existing methods, and four best classification models, to make visible that link prediction based on classification models works well across several varieties of complex networks and solve the link prediction problem with superior performance and with robustness. Show more

Keywords: Link prediction, geodesic distance, classification model, complex network, data mining

DOI: 10.3233/JIFS-179745

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6663-6675, 2020

Authors: Panda, Nibedan | Majhi, Santosh Kumar | Singh, Sarishma | Khanna, Abhirup

Article Type: Research Article

Abstract: Success behind nature inspired evolutionary metaheuristic algorithms lies in its seemly combination of operator’s castoff for smooth balance between exploration and exploitation. The deficit in such combination leads to untimely convergence of an algorithm, simultaneously failed to attain global optimum by stocking in local optimum. This work represents atypical algorithm termed as OBL-MO-SHO to improve the performance of existing SHO. To deal with more intricate realistic problems and to enhance the explorative and exploitative strength of SHO, we have integrated the oppositional learning concept with mutation operator. The proposed algorithm OBL-MO-SHO (oppositional spotted hyena optimizer with mutation operator) reveals promising …performance in terms of achieving global optimum and superior convergence rate which confirms its improved exploration and exploitation capability within searching region. To establish competency of proposed OBL-MO-SHO algorithm the same is appraised by means of standard functions set belongs to IEEE CEC 2017. The efficacy of said method has been proven by means of various performance metrics and the outcomes also compared with state-of-the-art algorithms. To scrutinize its uniqueness statistically, Friedman and Holms test has been performed as one non-parametric test. Additionally as an application to unravel real world intricate difficulties the said OBL-MO-SHO algorithm has been castoff to train wavelet neural network by considering datasets selected from UCI depository. The reported results unveils that the evolved OBL-MO-SHO might be one potential algorithm for enlightening different optimization difficulties effectively. Show more

Keywords: Swarm intelligence, spotted hyena optimizer, opposition-based learning, mutation operator, optimization, classification, wavelet neural network (WNN)

DOI: 10.3233/JIFS-179746

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6677-6690, 2020

Authors: Bhardwaj, Shubham | Geraldine Bessie Amali, D | Phadke, Amrut | Umadevi, K.S. | Balakrishnan, P.

Article Type: Research Article

Abstract: Metaheuristic algorithms are a family of algorithms that help solve NP-hard problems by providing near-optimal solutions in a reasonable amount of time. Galactic Swarm Optimization (GSO) is the state-of-the-art metaheuristic algorithm that takes inspiration from the motion of stars and galaxies under the influence of gravity. In this paper, a new scalable algorithm is proposed to help overcome the inherent sequential nature of GSO and helps the modified version of the GSO algorithm to utilize the full computing capacity of the hardware efficiently. The modified algorithm includes new features to tackle the problem of training an Artificial Neural Network. The …proposed algorithm is compared with Stochastic Gradient Descent based on performance and accuracy. The algorithm’s performance was evaluated based on per-CPU utilization on multiple platforms. Experimental results have shown that PGSO outperforms GSO and other competitors like PSO in a variety of challenging settings. Show more

Keywords: nature inspired metaheuristic, parallel computation, galactic swarm optimization, artificial neural networks

DOI: 10.3233/JIFS-179747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6691-6701, 2020

Authors: Rawat, Anuj | Jha, S.K. | Kumar, Bhavnesh | Mohan, Vijay

Article Type: Research Article

Abstract: This paper presents a fractional order nonlinear Proportional Integral Derivative (FONPID) controller to efficiently achieve the Maximum Power Point Tracking (MPPT) in Photovoltaic (PV) systems working under rapidly varying solar intensity and the temperature. In this paper, comparisons have been made among different techniques in respect of the extent of energy extracted from the photovoltaic (PV) system using MATLAB platforms. Gains of the proposed FONPID controllers are optimally tuned using a meta-heuristic based Elitist Teaching Learning Based Optimization (ETLBO) algorithm. The performance assessment of the FONPID controller is made in terms of efficiency, settling time, rise time and ripple. The …ETLBO tuned FONPID controller outperforms the other controller such as PID, Nonlinear PID (NPID), Fractional order PID (FOPID) and perturb and observe (P & O) technique. Therefore, in view of the meticulous investigation it is inferred that the proposed FONPID controller is an emerging MPPT technique with highest tracking efficiency and negligible ripple. Show more

Keywords: Fractional order nonlinear proportional-integral derivative (FONPID), maximum power point tracking (MPPT), elitist teaching learning based optimization (ETLBO)

DOI: 10.3233/JIFS-179748

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6703-6713, 2020

Authors: Chauhan, Urvashi | Singh, Vijander | Kumar, Bhavnesh | Rani, Asha

Article Type: Research Article

Abstract: This article proposes an improved multiverse optimization (IMVO) assisted maximum power point tacking (MPPT) algorithm for attaining maximum global power from photovoltaic system under partial shading condition. The proposed control scheme overcomes the difficulties occurring in traditional MPPT algorithms such as difficulty in attaining global maximum power under partial shading condition and incapability of handling oscillations in power at maximum power point. The algorithm is an amalgamation of IMVO and direct duty cycle control approach. The wormhole existence probability and time distance ratio are considered to be adaptive in improved MVO so as to ensure precise exploration and exploitation. In …this work multi crystal solar panel, KC130GT by M/S Kyocera, is analyzed for dynamic profiles of irradiance. Traditional P&O MPPT and improved particle swarm optimization MPPT (IPSO MPPT) are also designed for comparative analysis. The suggested IMVO MPPT proves to be superior in terms of power tracking performance, average efficiency and convergence capability as compared to other designed controllers. Show more

Keywords: Solar PV system, MPPT, multi verse optimization, improved multi verse optimization

DOI: 10.3233/JIFS-179749

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6715-6726, 2020

Authors: Nangrani, S.P. | Joshi, K.D.

Article Type: Research Article

Abstract: Engineering systems are nowadays expanding beyond expected limits and their complexity is also increasing. One of the largest integrated system is power system. Some well-designed power system experience strange situation and suffer through chaos owing to weak dynamic performance. Stability issues also haunt power system in such cases. Loss of stability investigation in power system leads to evidence of chaos as intermediate state quite often. Black swan theory tells us to be ready for unseen unruly behavior at any time. Noah and Joseph effects are also surfacing in almost every large expanding power system from different parts of world. Complexity …of system component behavior and complex stability boundaries pose a threat and ready to push power system where chaos is prevalent. It is debatable to see whether chaos leads to instability. This paper closely summarizes the chaos studies in light of reported research and advocates strongly the inclusion of advancement in chaos theory for detailed investigation post disturbance. This paper deals with a comprehensive review of strange behavior of nonlinear dynamic system and relevance of such studies for future anomalous behavior in the light of complexity science applied to engineering disasters such as blackouts. It is targeted to provide direction for futuristic complexity arising due to working on the brink of instability for economic reasons and to have certain preparation before inevitable blackouts, disasters and failures. A classified list of more than 50 relevant research publications is also given for quick reference. Show more

Keywords: Lyapunov exponent (LE), single machine infinite bus (SMIB), dynamic stability assessment (DSA)

DOI: 10.3233/JIFS-179750

Citation: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6727-6737, 2020