A Novel Genetic Algorithm Based k-means Algorithm for Cluster Analysis

El-Shorbagy, M. A.; Ayoub, A. Y.; Eldesoky, Islam M.; Mousa, Abd Allah A.

doi:10.1007/978-3-319-74690-6_10

Cited by 13 publications

(11 citation statements)

References 12 publications

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“…Tables 3 and 4 divide the security level and the cloud platform virtual layer security level. When the virtual layer of the cloud platform is in a Normal state, the current situation value of the virtual layer of the cloud platform is 0; if the virtual layer of the cloud platform is only attacked by the DNS, the current situation value of the virtual layer of the cloud platform is 0.5; if the virtual layer of the cloud platform is subjected to a variety of Attack, according to formula (8) weighted calculation, the current cloud platform virtual layer situation value S obtained.…”

Section: Experimental Simulation and Analysismentioning

confidence: 99%

“…Genetic Algorithm (GA) is a global search method based on natural selection and genetic principles [8] . It is often used to obtain the optimal solution of the objective function of the optimization problem.GA has strong search ability and high efficiency, so it is widely used to solve various optimization problems.But the biggest disadvantage of genetic algorithm is precocious, which is often called falling into local optimal value [9] .simulated annealing algorithm (Simulated Annealing,SA) is a local search optimization algorithm based on Monte Carlo iterative solution [10] .…”

Section: Introduction To Basic Algorithmsmentioning

confidence: 99%

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Research on security situation assessment algorithm under virtual technology of cloud platform

Liu¹,

Wang²

2021

MATEC Web Conf.

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With the increasing attacks and destructions against cloud platforms, the security guarantee mechanism of cloud platforms has also changed from traditional passive protectionto active defense. Situation assessment is a method that can actively analyze and evaluate thecurrent security risk status of the cloud platform, and is a key part of the whole process of situation awareness. This paper aims at a large number of virtual machines deployed in cloud platforms, on the basis of analysis and extraction of virtual layer security situation assessment elements, an improved adaptive genetic simulated annealing algorithm OAGSAA is proposed, and applied to BP neural network, which can effectively analyze and evaluate the security status of the virtual layer. Simulation experiment results show that the method has higher prediction accuracy and convergence speed, and can avoid falling into the local minimum.

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Section: Experimental Simulation and Analysismentioning

confidence: 99%

Section: Introduction To Basic Algorithmsmentioning

confidence: 99%

Research on security situation assessment algorithm under virtual technology of cloud platform

Liu¹,

Wang²

2021

MATEC Web Conf.

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“…The steps for the hybrid Genetic Algorithm and K-Means are as follows [18]: 1) Determining the parameters of the Genetic Algorithm 2) Determine a random number as many as the population as a candidate for the center of the cluster 3) Calculate the minimum distance and fitness of each cluster center 4) Carry out the selection, crossover, and mutation process 5) Repeat steps 3 and 4 until stable Hybrid Algoritma Genetika -FCM In the Fuzzy C-Means algorithm, the Genetic Algorithm is used to generate cluster center candidates by initializing the value of the degree of cluster membership so that it is not done randomly. The research steps of the genetic algorithm fuzzy clustering c-means can be seen in Figure 5 [19].…”

Section: Fig 4 Genetic Algorithm -K Means Flowchartmentioning

confidence: 99%

K-Means and Fuzzy C-Means Optimization using Genetic Algorithm for Clustering Questions

Sendari

Utama

Putri

et al. 2021

ijasca

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The grouping of data can be used in the development strategy of an educational game application. The process of grouping data that initially behaved differently into several groups that now behaved more uniformly. As well as grouping the data on the difficulty level of the questions on the educational game question board. This grouping of questions is needed to get the dominant values that will be the characteristics of each group of questions that exist. The clustering method is quite widely used to overcome problems related to data grouping. This clustering is a method of grouping based on the size of the proximity, the more accurate the cluster formed, the clearer the similarity of the difficulty level of the questions. Thus, educational game developers can determine the strategy for placing the existing questions more precisely. Many clustering methods can be used to group the data on this question, including K-Means and Fuzzy C-Means (FCM) which are then optimized using the Algorithm Genetics. From the results of the research conducted, optimization gives better results for clustering questions.

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“…In order to allow the DM to classify and coordinate solutions, some means of reducing/organizing the set of nondominated solutions are implemented to shrink the size of the Pareto optimal set, which facilitates finding the optimal operating alternative [45]. Several studies have concerned this issue by implementing filtering and cluster analysis to minimize the optimum size of the Pareto to a rational size, enabling the DM to choose the best compromise solution [46]. Algorithms of clustering can be divided into three categories: density-based clustering,…”

Section: Introductionmentioning

confidence: 99%

Constrained Multiobjective Equilibrium Optimizer Algorithm for Solving Combined Economic Emission Dispatch Problem

El-Shorbagy

Mousa

2021

Complexity

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This research implements a recent evolutionary-based algorithm of equilibrium optimizer to resolve the constrained combined economic emission dispatch problem. This problem has two objective functions that represent the minimizing of generation costs and minimizing the emission of environmental pollution caused by generators. The proposed algorithm integrates the dominant criteria for multiobjective functions that allow the decision-maker to detect all the Pareto boundaries of constrained combined economic emission dispatch problem. In order to save the effort for the decision-maker to select the best compromise alternative, a cluster study was carried out to minimize the size of the Pareto boundary to an acceptable size, representing all the characteristics of the main Pareto frontier. On the other hand, in order to deal with the infringement of constraints, a repair algorithm was used to preserve the viability of the particles. The proposed algorithm is applied to solve the standard 30-bus IEEE system with 6 generators to validate its robustness and efficiency to produce a well-distributed Pareto frontier for constrained combined economic emission dispatch problem. Compared with other studies, good results in solving constrained combined economic emission dispatch problem are obtained and a reasonable reduced Pareto set is found.

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A Novel Genetic Algorithm Based k-means Algorithm for Cluster Analysis

Cited by 13 publications

References 12 publications

Research on security situation assessment algorithm under virtual technology of cloud platform

Research on security situation assessment algorithm under virtual technology of cloud platform

K-Means and Fuzzy C-Means Optimization using Genetic Algorithm for Clustering Questions

Constrained Multiobjective Equilibrium Optimizer Algorithm for Solving Combined Economic Emission Dispatch Problem

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