Accuracy and Convergence Analysis of uFA-FastSLAM for Robot and Landmarks Position Estimation

In autonomous mobile robots, Simultaneous Localization and Mapping (SLAM) is a demanding and vital topic. One of two primary solutions of SLAM problem is FastSLAM. In terms of accuracy and convergence, FastSLAM is known to degenerate over time. Previous work has hybridized FastSLAM with a modified Firefly Algorithm (FA), called unranked Firefly Algorithm (uFA), to optimize the accuracy and convergence of the robot and landmarks position estimation. However, it has not shown the performance of the accuracy and convergence. Therefore, this work is done to present both mentioned performances of FastSLAM and uFA-FastSLAM to see which one is better. The result of the experiment shows that uFA-FastSLAM has successfully improved the accuracy (in other words, reduced estimation error) and the convergence consistency of FastSLAM. The proposed uFA-FastSLAM is superior compared to conventional FastSLAM in estimation of landmarks position and robot position with 3.30 percent and 7.83 percent in terms of accuracy model respectively. Furthermore, the proposed uFA-FastSLAM also exhibits better performances compared to FastSLAM in terms of convergence consistency by 93.49 percent and 94.20 percent for estimation of landmarks position and robot position respectively.

Modifikasi Firefly Algorithm Untuk Partial Shading pada Photovoltaic

Shadows of buildings or trees can partially cover the PV resulting in differences in solar radiation reception. Partial shading occurs when the PV module receives different solar radiation due to shadows of buildings or trees or clouds. This condition causes the output power of the PV array to decrease. Based on the PV curve, partial shading has a direct effect, so that a decrease in voltage or current causes a decrease in the output power of the PV. Because this requires a good control device. In this study, the method was compared without control (Uncontrolled), conventional PID (PID), PID tuned by Firefly Algorithm (PID-FA), and PID tuned by Modified Firefly Algorithm (PID-MFA). From the simulation results, it is found that the best voltage is obtained from the PID-MFA controller, the best voltage obtained from the PID-MFA controller is 1.2755 pu, the best current on the PID-MFA is 4,313; 3.67; 2,551 pu, and the maximum power is 3,253 pu. Thus it can be concluded that the best controller is PID-MFA. This research can later be used as a reference and other controllers are used to obtain an optimal controller.

An Optimized Utilization of Battery Backup in MANET Using Modified Firefly Algorithm

Temporary connection failures and route changes happen in a Mobile Ad Hoc Network (MANET). MANET enjoys extensive variety of applications like in tactical networks, Sensor networks. Much battery backup is required while tuning a node that is far from the sender node While compared to the node which is near in respect to sender, In this paper we are proposing an approach of optimized utilization of battery backup in MANET Battery Backup is a main constraint in mobile ad hoc networks Most of the battery is wasted in tuning to the networks repeatedly there by making the mobile node vulnerable to Jail. This paper emphasize on proper utilization of battery backup by varying the signal strength according the distance of the nodes. Modified FireFly Algorithm (MFFA) is greatly utilized in this research for boosting up of battery backup. The cluster head should maintain a table for battery backup and decision of task distribution will be based on this table.

An Intrusion Detection System Using Modified-Firefly Algorithm in Cloud Environment

The present era is being dominated by cloud computing technology which provides services to the users as per demand over the internet. Satisfying the needs of huge people makes the technology prone to activities which come up as a threat. Intrusion detection system (IDS) is an effective method of providing data security to the information stored in the cloud which works by analyzing the network traffic and informs in case of any malicious activities. In order to control high amount of data stored in cloud, data is stored as per relevance leading to distributed computing. To remove redundant data, the authors have implemented data mining process such as feature selection which is used to generate an optimum subset of features from a dataset. In this paper, the proposed IDS provides security working upon the idea of feature selection. The authors have prepared a modified-firefly algorithm which acts as a proficient feature selection method and enables the NSL-KDD dataset to consume less storage space by reducing dimensions as well as less training time with greater classification accuracy.

Feature Selection Models Based on Hybrid Firefly Algorithm with Mutation Operator for Network Intrusion Detection

Accurate intrusion detection is necessary to preserve network security. However, developing efficient intrusion detection system is a complex problem due to the nonlinear nature of the intrusion attempts, the unpredictable behaviour of network traffic, and the large number features in the problem space. Hence, selecting the most effective and discriminating feature is highly important. Additionally, eliminating irrelevant features can improve the detection accuracy as well as reduce the learning time of machine learning algorithms. However, feature reduction is an NPhard problem. Therefore, several metaheuristics have been employed to determine the most effective feature subset within reasonable time. In this paper, two intrusion detection models are built based on a modified version of the firefly algorithm to achieve the feature selection task. The first and, the second models have been used for binary and multiclass classification, respectively. The modified firefly algorithm employed a mutation operation to avoid trapping into local optima through enhancing the exploration capabilities of the original firefly. The significance of the selected features is evaluated using a Naïve Bayes classifier over a benchmark standard dataset, which contains different types of attacks. The obtained results revealed the superiority of the modified firefly algorithm against the original firefly algorithm in terms of the classification accuracy and the number of selected features under different scenarios. Additionally, the results assured the superiority of the proposed intrusion detection system against other recently proposed systems in both binary classification and multi-classification scenarios. The proposed system has 96.51% and 96.942% detection accuracy in binary classification and multi-classification, respectively. Moreover, the proposed system reduced the number of attributes from 41 to 9 for binary classification and to 10 for multi-classification.

An Arc Furnace as a Source of Voltage Disturbances—A Statistical Evaluation of Propagation in the Supply Network

This article presents the results of measuring Pst indicators at three points of a power system supplying a large source of voltage disturbances—an arc furnace. Measurements were made at three voltage levels: 30, 110, and 400 kV. Recorded values of Pst at each point were subjected to statistical analysis, the probability distributions were adjusted to their histograms, and the nature of changes in the basic parameters of these distributions with the distance from the source of disturbances was indicated. The adjustments of the distributions were made using a modified firefly algorithm.

Modified Firefly Algorithm and Fuzzy C-Mean Clustering Based Semantic Information Retrieval

As enormous volume of electronic data increased gradually, searching as well as retrieving essential info from the internet is extremely difficult task. Normally, the Information Retrieval (IR) systems present info dependent upon the user’s query keywords. At present, it is insufficient as large volume of online data and it contains less precision as the system takes syntactic level search into consideration. Furthermore, numerous previous search engines utilize a variety of techniques for semantic based document extraction and the relevancy between the documents has been measured using page ranking methods. On the other hand, it contains certain problems with searching time. With the intention of enhancing the query searching time, the research system implemented a Modified Firefly Algorithm (MFA) adapted with Intelligent Ontology and Latent Dirichlet Allocation based Information Retrieval (IOLDAIR) model. In this recommended methodology, the set of web documents, Face book comments and tweets are taken as dataset. By means of utilizing Tokenization process, the dataset pre-processing is carried out. Strong ontology is built dependent upon a lot of info collected by means of referring via diverse websites. Find out the keywords as well as carry out semantic analysis with user query by utilizing ontology matching by means of jaccard similarity. The feature extraction is carried out dependent upon the semantic analysis. After that, by means of Modified Firefly Algorithm (MFA), the ideal features are chosen. With the help of Fuzzy C-Mean (FCM) clustering, the appropriate documents are grouped and rank them. At last by using IOLDAIR model, the appropriate information’s are extracted. The major benefit of the research technique is the raise in relevancy, capability of dealing with big data as well as fast retrieval. The experimentation outcomes prove that the presented method attains improved performance when matched up with the previous system.