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Статті в журналах з теми "HYBRIDIZATION PSO+MSVM ALGORITHM"
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Masrom, Suraya, Abdullah Sani Abd Rahman, Nasiroh Omar, and Suriani Rapa’ee. "PSO-GAScript: A Domain-specific Scripting Language for Meta-heuristics Algorithm." International Journal of Emerging Technology and Advanced Engineering 12, no. 7 (July 4, 2022): 86–93. http://dx.doi.org/10.46338/ijetae0722_09.
Повний текст джерелаАнотація:
PSO-GAScript is a domain-specific scripting language designed to support easy and rapid implementation of meta-heuristics algorithms focused on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The programming language has been developed to allow the hybridization of the two meta-heuristics algorithms. Hybridizations between PSO and GA are proven to be a comprehensive tool for solving different kinds of optimization problems. Moreover, the two algorithms have achieved a remarkable improvement from the adaptation of dynamic parameterization. Nevertheless, implementing the suitable hybrid algorithms is a considerably difficult, which in most cases is time consuming. To the best of our knowledge, the existing tools are not adequately designed to enable users to easily develop the meta-heuristics hybridization of PSO-GA with dynamic parameterizations. This paper presents the fundamental research methodology of domain-specific scripting language from the language design, constructs and evaluations focused on the case of PSO-GA hybridization and dynamic parameterizations. The PSO-GAScript are shown to easily use with minimal number of codes lines and concisely describe the meta-heuristics algorithms in a directly publishable form.
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Gautam, Divya. "SECURING MOBILE ADHOC NETWORKS AND CLOUD ENVIRONMENT." International Journal of Engineering Technologies and Management Research 5, no. 2 (April 27, 2020): 84–89. http://dx.doi.org/10.29121/ijetmr.v5.i2.2018.617.
Повний текст джерелаАнотація:
Securing mobile adhoc networks and cloud environment in opposition to denial of service attack by examine and predict the network traffic. DDoS attacks are most important threats next to the accessibility of cloud services. Prevention mechanisms to protect next to DDoS attacks are not forever efficient on their own. Unite dissimilar method (load balancing, throttling and Honey pots) to build hybrid defense method, in meticulous with dissimilar cloud computing layers, is extremely recommended. In this paper, a variety of DDoS attacks have been presented. We as well highlighted the defense methods to counter attack dissimilar types DDoS attacks in the cloud environment. This paper proposes SVM-based algorithm to anomaly intrusion detection. A multiclass SVM algorithm with parameter optimized by PSO (MSVM-PSO) is accessible to find out a classifier to detect multiclass attacks. This paper will extend the proposed techniques to new computing environments Mobile Ad-Hoc Networks to detect anomalous physical or virtual nodes.
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Wang, Yuheng, Kashif Habib, Abdul Wadood, and Shahbaz Khan. "The Hybridization of PSO for the Optimal Coordination of Directional Overcurrent Protection Relays of the IEEE Bus System." Energies 16, no. 9 (April 26, 2023): 3726. http://dx.doi.org/10.3390/en16093726.
Повний текст джерелаАнотація:
The hybridization of PSO for the Optimal Coordination of Directional Overcurrent Protection Relays (DOPR) of the IEEE bus system proposes a new method for coordinating directional overcurrent protection relays in power systems. The method combines the hybrid particle swarm optimization (HPSO) algorithm and a heuristic PSO algorithm to find the minimum total operating time of the directional overcurrent protection relays with speed and accuracy. The proposed method is tested on the IEEE 4-bus, 6-bus, and 8-bus systems, and the results are compared with those obtained using traditional coordination methods. The collected findings suggest that the proposed method may produce better coordination and faster operation of DOPRs than the previous methods, with an increase of up to 74.9% above the traditional technique. The hybridization of the PSO algorithm and heuristic PSO algorithm offers a promising approach to optimize power system protection.
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Jain, Meetu, Vibha Saihjpal, Narinder Singh, and Satya Bir Singh. "An Overview of Variants and Advancements of PSO Algorithm." Applied Sciences 12, no. 17 (August 23, 2022): 8392. http://dx.doi.org/10.3390/app12178392.
Повний текст джерелаАнотація:
Particle swarm optimization (PSO) is one of the most famous swarm-based optimization techniques inspired by nature. Due to its properties of flexibility and easy implementation, there is an enormous increase in the popularity of this nature-inspired technique. Particle swarm optimization (PSO) has gained prompt attention from every field of researchers. Since its origin in 1995 till now, researchers have improved the original Particle swarm optimization (PSO) in varying ways. They have derived new versions of it, such as the published theoretical studies on various parameters of PSO, proposed many variants of the algorithm and numerous other advances. In the present paper, an overview of the PSO algorithm is presented. On the one hand, the basic concepts and parameters of PSO are explained, on the other hand, various advances in relation to PSO, including its modifications, extensions, hybridization, theoretical analysis, are included.
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Michaloglou, Alkmini, and Nikolaos L. Tsitsas. "A Brain Storm and Chaotic Accelerated Particle Swarm Optimization Hybridization." Algorithms 16, no. 4 (April 13, 2023): 208. http://dx.doi.org/10.3390/a16040208.
Повний текст джерелаАнотація:
Brain storm optimization (BSO) and particle swarm optimization (PSO) are two popular nature-inspired optimization algorithms, with BSO being the more recently developed one. It has been observed that BSO has an advantage over PSO regarding exploration with a random initialization, while PSO is more capable at local exploitation if given a predetermined initialization. The two algorithms have also been examined as a hybrid. In this work, the BSO algorithm was hybridized with the chaotic accelerated particle swarm optimization (CAPSO) algorithm in order to investigate how such an approach could serve as an improvement to the stand-alone algorithms. CAPSO is an advantageous variant of APSO, an accelerated, exploitative and minimalistic PSO algorithm. We initialized CAPSO with BSO in order to study the potential benefits from BSO’s initial exploration as well as CAPSO’s exploitation and speed. Seven benchmarking functions were used to compare the algorithms’ behavior. The chosen functions included both unimodal and multimodal benchmarking functions of various complexities and sizes of search areas. The functions were tested for different numbers of dimensions. The results showed that a properly tuned BSO–CAPSO hybrid could be significantly more beneficial over stand-alone BSO, especially with respect to computational time, while it heavily outperformed stand-alone CAPSO in the vast majority of cases.
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Adhikari, Ratnadip, and R. K. Agrawal. "Hybridization of Artificial Neural Network and Particle Swarm Optimization Methods for Time Series Forecasting." International Journal of Applied Evolutionary Computation 4, no. 3 (July 2013): 75–90. http://dx.doi.org/10.4018/jaec.2013070107.
Повний текст джерелаАнотація:
Recently, Particle Swarm Optimization (PSO) has evolved as a promising alternative to the standard backpropagation (BP) algorithm for training Artificial Neural Networks (ANNs). PSO is advantageous due to its high search power, fast convergence rate and capability of providing global optimal solution. In this paper, the authors explore the improvements in forecasting accuracies of feedforward as well as recurrent neural networks through training with PSO. Three widely popular versions of the basic PSO algorithm, viz. Trelea-I, Trelea-II and Clerc-Type1 are used to train feedforward ANN (FANN) and Elman ANN (EANN) models. A novel nonlinear hybrid architecture is proposed to incorporate the training strengths of all these three PSO algorithms. Experiments are conducted on four real-world time series with the three forecasting models, viz. Box-Jenkins, FANN and EANN. Obtained results clearly demonstrate the superior forecasting performances of all three PSO algorithms over their BP counterpart for both FANN as well as EANN models. Both PSO and BP based neural networks also achieved notably better accuracies than the statistical Box-Jenkins methods. The forecasting performances of the neural network models are further improved through the proposed hybrid PSO framework.
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Lenin, K. "HYBRIDIZATION OF ANT COLONY ALGORITHM AND PARTICLE SWARM OPTIMIZATION ALGORITHM FOR REDUCTION OF REAL POWER LOSS." International Journal of Research -GRANTHAALAYAH 6, no. 12 (December 31, 2018): 121–27. http://dx.doi.org/10.29121/granthaalayah.v6.i12.2018.1092.
Повний текст джерелаАнотація:
In this work Ant colony optimization algorithm (ACO) & particle swarm optimization (PSO) algorithm has been hybridized (called as APA) to solve the optimal reactive power problem. In this algorithm, initial optimization is achieved by particle swarm optimization algorithm and then the optimization process is carry out by ACO around the best solution found by PSO to finely explore the design space. In order to evaluate the proposed APA, it has been tested on IEEE 300 bus system and compared to other standard algorithms. Simulations results show that proposed APA algorithm performs well in reducing the real power loss.
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Bi, Ya, Anthony Lam, Huiqun Quan, Hui Liu, and Cunfa Wang. "A comprehensively improved particle swarm optimization algotithm to guarantee particle activity." Izvestiya vysshikh uchebnykh zavedenii. Fizika, no. 5 (2021): 94–101. http://dx.doi.org/10.17223/00213411/64/5/94.
Повний текст джерелаАнотація:
The particle swarm optimization algorithm has the disadvantages, for instance, the convergence viscosity of the algorithm is reduced at the post evolution phase, the optimization search efficiency is reduced, the algorithm is easy to be inserted with local extremum during the calculation of complex problem of high-dimensional multiple extremum, and the convergence thereof is low. As to the disadvantage of the PSO, we proposed a particle swarm optimization of comprehensive improvement strategy, which is a simple particle swarm optimization with dynamic adaptive hybridization of extremum disturbance and cross (ecds-PSO algorithm). This new comprehensive improved particle swarm algorithm discards the particle velocity and reduces the PSO from the second order to the first order difference equation. The evolutionary process is only controlled by the variables of the particles position. The hybridization operation of increasing the extremum disturbance and introducing genetic algorithm can accelerate the particles to overstep the local extremum. The mathematical derivation and a plurality of comparative experiment provide us the following information: the improved particle swarm optimization is a simple and effective optimization algorithm which can improve the algorithm accuracy, convergence viscosity and ability of avoiding the local extremum, and effectively reduce the calculation complexity.
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Zhang, Yudong, Shuihua Wang, and Genlin Ji. "A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications." Mathematical Problems in Engineering 2015 (2015): 1–38. http://dx.doi.org/10.1155/2015/931256.
Повний текст джерелаАнотація:
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.
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Xiao, Heng, Yokoya, and Toshiharu Hatanaka. "Multifactorial Particle Swarm Optimization Enhanced by Hybridization With Firefly Algorithm." International Journal of Swarm Intelligence Research 12, no. 3 (July 2021): 172–87. http://dx.doi.org/10.4018/ijsir.2021070108.
Повний текст джерелаАнотація:
In recent years, evolutionary multitasking has received attention in the evolutionary computation community. As an evolutionary multifactorial optimization method, multifactorial evolutionary algorithm (MFEA) is proposed to realize evolutionary multitasking. One concept called the skill factor is introduced to assign a preferred task for each individual in MFEA. Then, based on the skill factor, there are some multifactorial optimization solvers including swarm intelligence that have been developed. In this paper, a PSO-FA hybrid model with a model selection mechanism triggered by updating the personal best memory is applied to multifactorial optimization. The skill factor reassignment is introduced in this model to enhance the search capability of the hybrid swarm model. Then numerical experiments are carried out by using nine benchmark problems based on typical multitask situations and by comparing with a simple multifactorial PSO to show the effectiveness of the proposed method.
Дисертації з теми "HYBRIDIZATION PSO+MSVM ALGORITHM"
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SINGH, BHUPINDER. "A HYBRID MSVM COVID-19 IMAGE CLASSIFICATION ENHANCED USING PARTICLE SWARM OPTIMIZATION." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18864.
Повний текст джерелаАнотація:
COVID-19 (novel coronavirus disease) is a serious illness that has killed millions of
civilians and affected millions around the world. Mostly as result, numerous
technologies that enable both the rapid and accurate identification of COVID-19
illnesses will provide much assistance to healthcare practitioners. A machine learning-
based approach is used for the detection of COVID-19. In general, artificial intelligence
(AI) approaches have yielded positive outcomes in healthcare visual processing and
analysis. CXR is the digital image processing method that plays a vital role in the
analysis of Covid-19 disease. Due to the maximum accessibility of huge scale annotated
image databases, excessive success has been done using multiclass support vector
machines for image classification. Image classification is the main challenge to detect
medical diagnosis. The existing work used CNN with a transfer learning mechanism
that can give a solution by transferring information from GENETIC object recognition
tasks. The DeTrac method has been used to detect the disease in CXR images. DeTrac
method accuracy achieved 93.1~ 97 percent. In this proposed work, the hybridization
PSO+MSVM method has worked with irregularities in the CXR images database by
studying its group distances using a group or class mechanism. At the initial phase of
the process, a median filter is used for the noise reduction from the image. Edge
detection is an essential step in the process of COVID-19 detection. The canny edge
detector is implemented for the detection of edges in the chest x-ray images. The PCA
(Principal Component Analysis) method is implemented for the feature extraction
phase. There are multiple features extracted through PCA and the essential features are
optimized by an optimization technique known as swarm optimization is used for
feature optimization. For the detection of COVID-19 through CXR images, a hybrid
multi-class support vector machine technique is implemented. The PSO (particle swarm
optimization) technique is used for feature optimization. The comparative analysis of
various existing techniques is also depicted in this work. The proposed system has
achieved an accuracy of 97.51 percent, SP of 97.49 percent, and 98.0 percent of SN.
The proposed system is compared with existing systems and achieved better
performance and the compared systems are DeTrac, GoogleNet, and SqueezeNet.
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Agbugba, Emmanuel Emenike. "Hybridization of particle Swarm Optimization with Bat Algorithm for optimal reactive power dispatch." Diss., 2017. http://hdl.handle.net/10500/23630.
Повний текст джерелаАнотація:
This research presents a Hybrid Particle Swarm Optimization with Bat Algorithm (HPSOBA) based
approach to solve Optimal Reactive Power Dispatch (ORPD) problem. The primary objective of
this project is minimization of the active power transmission losses by optimally setting the control
variables within their limits and at the same time making sure that the equality and inequality
constraints are not violated. Particle Swarm Optimization (PSO) and Bat Algorithm (BA)
algorithms which are nature-inspired algorithms have become potential options to solving very
difficult optimization problems like ORPD. Although PSO requires high computational time, it
converges quickly; while BA requires less computational time and has the ability of switching
automatically from exploration to exploitation when the optimality is imminent. This research
integrated the respective advantages of PSO and BA algorithms to form a hybrid tool denoted as
HPSOBA algorithm. HPSOBA combines the fast convergence ability of PSO with the less
computation time ability of BA algorithm to get a better optimal solution by incorporating the BA’s
frequency into the PSO velocity equation in order to control the pace. The HPSOBA, PSO and BA algorithms were implemented using MATLAB programming language and tested on three (3)
benchmark test functions (Griewank, Rastrigin and Schwefel) and on IEEE 30- and 118-bus test
systems to solve for ORPD without DG unit. A modified IEEE 30-bus test system was further used
to validate the proposed hybrid algorithm to solve for optimal placement of DG unit for active
power transmission line loss minimization. By comparison, HPSOBA algorithm results proved to
be superior to those of the PSO and BA methods.
In order to check if there will be a further improvement on the performance of the HPSOBA, the
HPSOBA was further modified by embedding three new modifications to form a modified Hybrid
approach denoted as MHPSOBA. This MHPSOBA was validated using IEEE 30-bus test system to
solve ORPD problem and the results show that the HPSOBA algorithm outperforms the modified
version (MHPSOBA).Electrical and Mining Engineering
M. Tech. (Electrical Engineering)
Частини книг з теми "HYBRIDIZATION PSO+MSVM ALGORITHM"
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Kumar Pandey, Anurag, Aditya Vikram Dang, Naman Taneja, Uma Nangia, and N. K. Jain. "Hybrid Weight Strategy for Particle Swarm Optimization." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220758.
Повний текст джерелаАнотація:
Particle Swarm Optimization algorithm (PSO) is found to be an effective meta-heuristic swarm-based algorithm in solving modern time problems. Various improvements have been proposed in this algorithm in terms of internal computation, acceleration coefficients, stopping criteria, hybridization, velocity upgradation etc. The objective of this paper is to implement hybrid weights and, therefore, improve the quality of PSO algorithm. In the case of hybrid weights, we have combined two weights at a time. These weights are mixed in various but not in equal proportions and are tested against ten standard testing functions along with the pre-existing weights. By using this collection, we have analysed them on three parameters-mean, standard deviation, and minimum value achieved. Later on, after analysing the data, we found out that hybrid weights are an overall better option with respect to the pre-existing weights.
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Karami, Amin. "A Novel Fuzzy Anomaly Detection Algorithm Based on Hybrid PSO-Kmeans in Content-Centric Networking." In Advances in Computational Intelligence and Robotics, 518–50. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9474-3.ch017.
Повний текст джерелаАнотація:
In Content-Centric Networks (CCNs) as a promising network architecture, new kinds of anomalies will arise. Usually, clustering algorithms would fit the requirements for building a good anomaly detection system. K-means is a popular anomaly detection method; however, it suffers from the local convergence and sensitivity to selection of the cluster centroids. This chapter presents a novel fuzzy anomaly detection method that works in two phases. In the first phase, authors propose an hybridization of Particle Swarm Optimization (PSO) and K-means algorithm with two simultaneous cost functions as well-separated clusters and local optimization to determine the optimal number of clusters. When the optimal placement of clusters centroids and objects are defined, it starts the second phase. In this phase, the authors employ a fuzzy approach by the combination of two distance-based methods as classification and outlier to detect anomalies in new monitoring data. Experimental results demonstrate that the proposed method can yield high accuracy as compared to preexisting algorithms.
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Zarandi, Mohammad Hossein Fazel, Milad Avazbeigi, and Meysam Alizadeh. "A Neuro-Fuzzy Expert System Trained by Particle Swarm Optimization for Stock Price Prediction." In Cross-Disciplinary Applications of Artificial Intelligence and Pattern Recognition, 633–50. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-429-1.ch031.
Повний текст джерелаАнотація:
In today’s competitive markets, prediction of financial variables has become a critical issue. Especially in stock market analysis where a wrong prediction may result in a big loss in terms of time and money, having a robust prediction is a crucial issue. To model the chaotic, noisy, and evolving behavior of stock market data, new powerful methods should be developed. Soft Computing methods have shown a great confidence in such environments where there are many uncertain factors. Also it has been observed through many experiments that the hybridization of different soft computing techniques such as fuzzy logic, neural networks, and meta-heuristics usually results in better results than simply using one method. This chapter presents an adaptive neuro-fuzzy inference system (ANFIS), trained by the particle swarm optimization (PSO) algorithm for stock price prediction. Instead of previous works that have emphasized on gradient base or least square (LS) methods for training the neural network, four different strategies of PSO are implemented: gbest, lbest-a, lbest-b, and Euclidean. In the proposed fuzzy rule based system some technical and fundamental indexes are applied as input variables. In order to generate membership functions (MFs), a robust noise rejection clustering algorithm is developed. The proposed neuro-fuzzy model is applied for an automotive part-making manufactory in an Asia stock market. The results show the superiority of the proposed model in comparison with the available models in terms of error minimization, robustness, and flexibility.
Тези доповідей конференцій з теми "HYBRIDIZATION PSO+MSVM ALGORITHM"
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Binu, D., and Aloysius George. "KF-PSO: Hybridization of particle swarm optimization and kernel-based fuzzy C means algorithm." In 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, 2013. http://dx.doi.org/10.1109/icacci.2013.6637224.
Повний текст джерела