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Добірка наукової літератури з теми "FUZZY-GSA"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "FUZZY-GSA"

Shyla, S. Immaculate, and S. S. Sujatha. "An Efficient Automatic Intrusion Detection in Cloud Using Optimized Fuzzy Inference System." International Journal of Information Security and Privacy 14, no. 4 (October 2020): 22–41. http://dx.doi.org/10.4018/ijisp.2020100102.

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Анотація:

Security incidents such as denial of service (DoS), scanning, malware code injection, viruses, worms, and password cracking are becoming common in a cloud environment that affects companies and may produce a financial loss if not detected in time. Such problems are handled by presenting an intrusion detection system (IDS) into the cloud. The existing cloud IDSs affect low detection accuracy, high false detection rate, and execution time. To overcome this problem, in this article, a gravitational search algorithm-based fuzzy inference system (GSA-FIS) is developed as intrusion detection. In this approach, fuzzy parameters are optimized using GSA. The proposed consist of two modules namely; possibilistic fuzzy c-means (PFCM) based clustering, training based on the GSA-FIS, and testing process. Initially, the incoming data is pre-processed and clustered with the help of PFCM. PFCM detects the noise of fuzzy c-means clustering (FCM), then conquers the coincident cluster problem of possibilistic fuzzy c-means (PCM) and eradicate the row sum constraints of fuzzy possibilistic c-means clustering (FPCM). After the clustering process, the clustered data is given to the optimized fuzzy inference system (OFIS). Here, normal and abnormal data are identified by the fuzzy score, while the training is done by the GSA through optimizing the entire fuzzy system. In this approach, four types of abnormal data are detected namely- probe, remote to local (R2L), user to root (U2R), and DoS. Simulation results show that the performance of the proposed GSA-FIS based IDS outperforms that of the different schemes in terms of precision, recall and F-measure.

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Chao, Chun-Tang, Ming-Tang Liu, Juing-Shian Chiou, Yi-Jung Huang, and Chi-Jo Wang. "A GSA-based adaptive fuzzy PID-controller for an active suspension system." Engineering Computations 33, no. 6 (August 1, 2016): 1659–67. http://dx.doi.org/10.1108/ec-08-2015-0240.

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Purpose – The purpose of this paper is to propose a novel design for determining the optimal hybrid fuzzy PID-controller of an active automobile suspension system, employing the gravitational search algorithm (GSA). Design/methodology/approach – The hybrid fuzzy PID-controller structure is an improvement to fuzzy PID-controller by incorporating a fast learning PID-controller. Findings – The GSA can adjust the parameters of the PID-controller to achieve the optimal performance. Research limitations/implications – The GSA may have the advantage of quick convergence, but the required computation may be intensive. Practical implications – The simulation results demonstrate the effectiveness of the proposed approach on active automobile suspension system. Originality/value – In order to demonstrate the theoretical guarantee of the proposed method, comparisons with particle swarm optimization or other methods has also been carried out.

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Bardamova, Marina, Anton Konev, Ilya Hodashinsky, and Alexander Shelupanov. "A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm." Symmetry 10, no. 11 (November 7, 2018): 609. http://dx.doi.org/10.3390/sym10110609.

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This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

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Yu, Xiaobing, Xianrui Yu, and Xueying Zhang. "Case-based reasoning adaptation based on fuzzy gravitational search algorithm for disaster emergency plan." Journal of Intelligent & Fuzzy Systems 40, no. 6 (June 21, 2021): 11007–22. http://dx.doi.org/10.3233/jifs-202132.

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Disasters can result in substantial destructive damages to the world. Emergency plan is vital to deal with these disasters. It is still difficult for the traditional CBR to generate emergency plans to meet requirements of rapid responses. An integrated system including Case-based reasoning (CBR) and gravitational search algorithm (GSA) is proposed to generate the disaster emergency plan. Fuzzy GSA (FGSA) is developed to enhance the convergence ability and accomplish the case adaptation in CBR. The proposed algorithm dynamically updates the main parameters of GSA by introducing a fuzzy system. The FGSA-CBR system is proposed, in which fitness function is defined based on the effectiveness of disaster emergency management. The comparison results have revealed that the proposed algorithm has good performances compared with the original GSA and other algorithms. A gas leakage accident is taken as an empirical study. The results have demonstrated that the FGSA-CBR has good performances when generating the disaster emergency plan. The combination of CBR and FGSA can realize the case adaptation, which provides a useful approach to the real applications.

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Pramana, Setia, and Imam Habib Pamungkas. "Improvement Method of Fuzzy Geographically Weighted Clustering using Gravitational Search Algorithm." Jurnal Ilmu Komputer dan Informasi 11, no. 1 (February 28, 2018): 10. http://dx.doi.org/10.21609/jiki.v11i1.580.

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Geo-demographic analysis (GDA) is a useful method to analyze information based on location, utilizing several spatial analysis explicitly. One of the most efficient and commonly used method is Fuzzy Geographically Weighted Clustering (FGWC). However, it has a limitation in obtaining local optimal solution in the centroid initialization. A novel approach integrating Gravitational Search Algorithm (GSA) with FGWC is proposed to obtain global optimal solution leading to better cluster quality. Several cluster validity indexes are used to compare the proposed methods with the FGWC using other optimization approaches. The study shows that the hybrid method FGWC-GSA provides better cluster quality. Furthermore, the method has been implemented in R package spatialClust.

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Mashfufah, Syayidati, Indah Manfaati Nur, and Moh Yamin Darsyah. "Fuzzy Geographically Weighted Clustering dengan Gravitational Search Algorithm pada Kasus Penyandang Masalah Kesejahteraan Sosial di Provinsi Jawa Tengah." Jurnal Litbang Edusaintech 2, no. 1 (May 31, 2021): 27–36. http://dx.doi.org/10.51402/jle.v2i1.10.

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One of the indicators of the success of social welfare development in Central Java was decreasing the population of people with social welfare problems (PMKS). One exertion that can be done was grouping or clustering the areas in Central Java-based on 26 indicators of PMKS. Fuzzy Geographically Weighted Clustering (FGWC) algorithm is a clustering analysis that observing the effect of the area. However, FGWC has a limitation in the initialization centroid phase that makes it trapped to local optimal. The limitation can be addressed with the Gravitational Search Algorithm (GSA) approach. The purpose of GSA was to optimize the value objective function. This research applied FGWC-GSA on PMKS in Central Java Province contained 26 indicators. Some validity indexes were applied to determine the best cluster. This research clustering the areas of Central Java into two clusters. The first cluster contained 24 districts and cities, and the second cluster contained 11 districts

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Hashim, H. A., and M. A. Abido. "Fuzzy Controller Design Using Evolutionary Techniques for Twin Rotor MIMO System: A Comparative Study." Computational Intelligence and Neuroscience 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/704301.

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This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed.

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Kherabadi, Hossein Azadi, Sepehr Ebrahimi Mood, and Mohammad Masoud Javidi. "Mutation: A New Operator in Gravitational Search Algorithm Using Fuzzy Controller." Cybernetics and Information Technologies 17, no. 1 (March 1, 2017): 72–86. http://dx.doi.org/10.1515/cait-2017-0006.

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Abstract Gravitational Search Algorithm (GSA) isanovel meta-heuristic algorithm. Despite it has high exploring ability, this algorithm faces premature convergence and gets trapped in some problems, therefore it has difficulty in finding the optimum solution for problems, which is considered as one of the disadvantages of GSA. In this paper, this problem has been solved through definingamutation function which uses fuzzy controller to control mutation parameter. The proposed method has been evaluated on standard benchmark functions including unimodal and multimodal functions; the obtained results have been compared with Standard Gravitational Search Algorithm (SGSA), Gravitational Particle Swarm algorithm (GPS), Particle Swarm Optimization algorithm (PSO), Clustered Gravitational Search Algorithm (CGSA) and Real Genetic Algorithm (RGA). The observed experiments indicate that the proposed approach yields better results than other algorithms compared with it.

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Javanbakht, Mohammad, and Mohammad Javad Mahmoodabadi. "Achieving More Stringent Levels of Comfort via an Adaptive Fuzzy Controller Optimized by the Gravitational Search Algorithm for a Half-Body Car Model." Volume 24, No 3, September 2019 24, no. 3 (September 2019): 567–77. http://dx.doi.org/10.20855/jav.2019.24.31399.

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An optimal adaptive fuzzy controller is designed to achieve more stringent levels of comfort for a half-body car model. This aim will be fulfilled by reducing road disturbances and decreasing the acceleration of the body. The proposed controller consists of two adaptive fuzzy controllers with two fuzzy systems. Each one has two inputs, one output and twenty five linguistic fuzzy IF-THEN rules. Every input has five Gaussian membership functions and uses the product inference engine, singleton fuzzifier and the centre average defuzzifier. In order to determine the optimal parameters for the Adaptive Fuzzy Controller (AFC), the Gravitational Search Algorithm (GSA) is applied. The relative displacement between spring mass and tire, along with the acceleration of the body, are the two objective functions being applied in the optimization algorithm. The results illustrate the superiority of the proposed optimal adaptive fuzzy controller in comparison with traditional controllers.

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Bounar, N., S. Labdai, and A. Boulkroune. "PSO–GSA based fuzzy sliding mode controller for DFIG-based wind turbine." ISA Transactions 85 (February 2019): 177–88. http://dx.doi.org/10.1016/j.isatra.2018.10.020.

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Дисертації з теми "FUZZY-GSA"

KUMAR, ARVIND. "FUZZY CLUSTERING FOR COLOR IMAGE SEGMENTATION USING GRAVITATIONAL SEARCH ALGORITHM." Thesis, 2016. http://dspace.dtu.ac.in:8080/jspui/handle/repository/15400.

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Clustering is a key activity in numerous data mining applications such as information retrieval, text mining, image segmentation, etc. This research work proposes a clustering approach, Fuzzy-GSA, based on gravitational search algorithm (GSA). In the proposed Fuzzy-GSA approach, a fuzzy inference system is developed to effectively control the parameters of GSA. The performance of the Fuzzy-GSA algorithm is evaluated against four benchmark datasets from the UC Irvine repository. The results illustrate that the Fuzzy-GSA approach attains the highest quality clustering over the selected datasets when compared with several other clustering algorithms namely, k-means, particle swarm optimization (PSO), gravitational search algorithm (GSA) and, combined gravitational search algorithm and k-means approach (GSA-KM) In this paper, we propose a new hybrid approach for image segmentation. The proposed approach exploits fuzzy GSA for clustering image pixels into homogeneous regions. In order to improve the performance of fuzzy clustering to cope with segmentation problems, we employ gravitational search algorithm which is inspired by Newton’s rule of gravity. Gravitational search algorithm is incorporated into fuzzy GSA to take advantage of its ability to find an optimum cluster center which minimizes the fitness function of fuzzy GSA. Experimental results show effectiveness of the proposed method in segmentation different types of images as compared to classical fuzzy Algorithm

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Частини книг з теми "FUZZY-GSA"

Karthikumar, K., V. Senthil Kumar, M. Karuppiah, N. Udhaya Raj, A. Arunbalaj, and S. C. Vijayakumar. "A Novel Monitoring Arrangement for Single and Multiple Power Quality Occasions Calculation and Classification in Supply System: A GSA-FUZZY Strategy." In Sixth International Conference on Intelligent Computing and Applications, 115–36. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1335-7_11.

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Garg, Harish. "A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data." In Handbook of Research on Artificial Intelligence Techniques and Algorithms, 620–54. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-7258-1.ch020.

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The main objective of this chapter is to present a novel hybrid GA-GSA algorithm to permit the reliability analyst to increase the performance of the system by utilizing the uncertain data. Since the analysis based on the collected data mostly contains a lot of uncertainties, the corresponding results obtained do not tell the exact nature of the system. Therefore, to handle this issue, the proposed algorithm maximizes the Reliability, Availability, and Maintainability (RAM) parameters simultaneously for increasing the performance and productivity of the system. The conflicts between the objectives are resolved with the help of intuitionistic fuzzy set theory. The optimal design parameters corresponding to each component of the system are evaluated by solving a nonlinear optimization problem and compared their results with other methods. The stability of these optimal parameters is justified by means of pooled t-test statistics. Based on these optimal design parameters, an investigation has been done for finding the most critical component of the system for saving money, manpower, and time, as well as increasing the performance of the system. Finally, to illustrate the methodology, a numerical example is studied.

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Srivastava, Vishal, and Smriti Srivastava. "A Comprehensive Review of Optimization Algorithms for Nonlinear Systems." In New Frontiers in Communication and Intelligent Systems, 535–46. Soft Computing Research Society, 2021. http://dx.doi.org/10.52458/978-81-95502-00-4-55.

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The practical problems are full of nonlinearity and uncertainty due to their dynamic behavior. The uncertainties, parameters variations and other constraints make nonlinear systems very complex. To deal with such system dynamics and uncertainties, soft computing techniques are widely used. Optimization algorithms are one of the most effective and simple soft computing techniques.In literature, several controllers and control mythologies are being discussed to evaluate the system performance. Optimization algorithms are one of the popular soft computing techniques, used with different controllers like conventional PID, fuzzy logic, ANN, and many others to handle system nonlinearities and enhance the performance. These optimization algorithms not only improve the performance effectively but also gave robust response towards the nonlinearities. The proper selection of algorithm is a very important aspect to find the best solution. Here three categories of algorithms i.e. from swarm-based algorithms particle swarm optimization (PSO), grasshopper optimization algorithm (GOA), grey wolf optimization (GWO), whale optimization algorithm (WOA), gravitational search algorithm (GSA) from physics based and teaching learning based optimization (TLBO) from human-based have been reviewed for nonlinear systems. Most of the algorithms are suffering from either of abilities i.e. exploration or exploitation so sometimes they are not able to give optimal solution. To overcome with this problem, recently hybrid approach of algorithms has been widely used in which the better sides of the individual algorithms are utilized.

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Тези доповідей конференцій з теми "FUZZY-GSA"

Askari, Hossein, and Seyed-Hamid Zahiri. "Data classification using fuzzy-GSA." In 2011 International eConference on Computer and Knowledge Engineering (ICCKE). IEEE, 2011. http://dx.doi.org/10.1109/iccke.2011.6413315.

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Farsangi, Sina Ebrahimi, Esmat Rashedi, and Malihe M. Farsangi. "Multi-objective VAr planning using fuzzy-GSA." In 2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC). IEEE, 2017. http://dx.doi.org/10.1109/csiec.2017.7940180.

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Gupta, Chhavi, and Sanjeev Jain. "Multilevel fuzzy partition segmentation of satellite images using GSA." In 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT). IEEE, 2014. http://dx.doi.org/10.1109/icspct.2014.6884903.

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Rohmah, Dewi Syifaur, S. Dewi Retno Sari, and K. Vika Yugi. "Clustering human development index data with gravitational search algorithm-fuzzy 4-means (GSA-F4M)." In THE THIRD INTERNATIONAL CONFERENCE ON MATHEMATICS: Education, Theory and Application. AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0039661.

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Venkat, R., and K. Satyanarayan Reddy. "Dealing Big Data using Fuzzy C-Means (FCM) Clustering and Optimizing with Gravitational Search Algorithm (GSA)." In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2019. http://dx.doi.org/10.1109/icoei.2019.8862673.

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Chaouali, H., H. Othmani, D. Mezghani, H. Jouini, and A. Mami. "Fuzzy logic control scheme for a 3 phased asynchronous machine fed by Kaneka GSA-60 PV panels." In 2016 7th International Renewable Energy Congress (IREC). IEEE, 2016. http://dx.doi.org/10.1109/irec.2016.7478893.

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Debnath, Manoj Kumar, Ranjan Kumar Mallick, Sadhana Das, and Aditya Aman. "Gravitational search algorithm (GSA) optimized fuzzy-PID controller design for load frequency control of an interconnected multi-area power system." In 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT). IEEE, 2016. http://dx.doi.org/10.1109/iccpct.2016.7530205.

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Faybishenko, Boris, Juliane Mueller, Juliane Mueller, Reetik Sahu, Reetik Sahu, Jangho Park, Jangho Park, et al. "FUZZY RULE-BASED SYSTEMS FOR MULTIVARIATE AND UNIVARIATE HYDROLOGICAL FORECASTING." In GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-358604.

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Lauters, Jonathan David, and Gil Nelson. "LINKING DATA SILOS VIA FUZZY MATCHING ALGORITHMS." In GSA Annual Meeting in Denver, Colorado, USA - 2016. Geological Society of America, 2016. http://dx.doi.org/10.1130/abs/2016am-285169.

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Chung, Sang Yong, Venkatramanan Senapathi, Kye-Hun Park, Selvam Sekar, Hussam Eldin Elzain, JooHyeong Son, and Elyorbek Khakimov. "EVALUATION OF GROUNDWATER CONTAMINATION USING GEOCHEMICAL ANALYSES AND FUZZY TECHNIQUE." In GSA Annual Meeting in Seattle, Washington, USA - 2017. Geological Society of America, 2017. http://dx.doi.org/10.1130/abs/2017am-305024.

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