scholarly journals Load Balancing Selection Method and Simulation in Network Communication Based on AHP-DS Heterogeneous Network Selection Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Weiwei Xiao
Keyword(s):  
Genetic Algorithm ◽  
Decision Tree ◽  
Service Quality ◽  
Balancing Selection ◽  
Fitness Function ◽  
Training Data ◽  
Network Selection ◽  
Network Access ◽  
Selection Algorithm ◽  
Decision Algorithm

This article proposes an Analytic Hierarchy Process Dempster-Shafer (AHP-DS) and similarity-based network selection algorithm for the scenario of dynamic changes in user requirements and network environment; combines machine learning with network selection and proposes a decision tree-based network selection algorithm; combines multiattribute decision-making and genetic algorithm to propose a weighted Gray Relation Analysis (GRA) and genetic algorithm-based network access decision algorithm. Firstly, the training data is obtained from the collaborative algorithm, and it is used as the training set, and the network attributes are used as the attribute set, and the continuous attributes are discretized by dichotomization, and the attribute that can make the greatest information gain is selected as the division feature, and a decision tree with strong generalization ability is finally obtained, which is used as the decision basis for network access selection. The simulation results show that the algorithm proposed in this thesis can effectively improve user service quality under three services, and the algorithm is simple and effective with low complexity. It first uses AHP-DS hierarchical analysis to establish a recursive hierarchy for the network selection problem and obtains the subjective weights of network attributes through the judgment matrix. Then, it uses a genetic algorithm to adjust the subjective weight, defines the fitness function in the genetic algorithm-based on gray correlation analysis, adjusts the weights of the selection operator, crossover operator, and variation operator in the genetic algorithm, and gets the network with the largest fitness as the target network, which can effectively improve the user service quality.

scholarly journals Network selection algorithm based on decision tree in heterogeneous wireless networks

2018 ◽  
Vol 189 ◽  
pp. 04010 ◽  
Author(s):  
Wang-hong Li ◽  
Zhu Qi
Keyword(s):  
Decision Tree ◽  
Information Gain ◽  
Heterogeneous Wireless Networks ◽  
Training Data ◽  
Network Selection ◽  
User Requirements ◽  
Selection Algorithm ◽  
Training Set ◽  
Service Characteristics

A network selection algorithm based on Decision Tree is proposed to solve the problem. Users can select the appropriate network according to their service characteristics and requirements when they decide which network to access. First, we get the training data under the Interactive Class service from the synergetic algorithm which can be used for training set. The network attributes are used for attribute set. And then we can choose the attribute with the largest information gain as the division attribute after the discretization of continuous features by the bisection method. Keep going this step recursively, we can finally get a decision tree with high generalization ability by which we can make the network selection. Simulation results show that the algorithm we proposed is simple and effective and demonstrate the effectiveness of our scheme in improving the quality of service according to the user requirements under the Interactive Class service.

scholarly journals A new genetic algorithm specifically based on mutation and selection

2007 ◽  
Vol 39 (01) ◽  
pp. 141-161 ◽  
Author(s):  
L. Rigal ◽  
L. Truffet
Keyword(s):  
Genetic Algorithm ◽  
Selection Pressure ◽  
Large Deviation ◽  
Fitness Function ◽  
Critical Value ◽  
Gradient Algorithm ◽  
Selection Algorithm ◽  
Local Maxima ◽  
Selection Operator ◽  
Mutation And Selection

In this paper we propose a new genetic algorithm specifically based on mutation and selection in order to maximize a fitness function. This mutation-selection algorithm behaves as a gradient algorithm which converges to local maxima. In order to obtain convergence to global maxima we propose a new algorithm which is built by randomly perturbing the selection operator of the gradient-like algorithm. The perturbation is controlled by only one parameter: that which allows the selection pressure to be governed. We use the Markov model of the perturbed algorithm to prove its convergence to global maxima. The arguments used in the proofs are based on Freidlin and Wentzell's (1984) theory and large deviation techniques also applied in simulated annealing. Our main results are that (i) when the population size is greater than a critical value, the control of the selection pressure ensures the convergence to the global maxima of the fitness function, and (ii) the convergence also occurs when the population is the smallest possible, i.e. 1.

A Novel Heterogeneous Wireless Network Selection Algorithm Based on INFAHP and IGRA

2020 ◽  
Vol 20 (03) ◽  
pp. 2050010
Author(s):  
HEWEI YU ◽  
MEIYUAN GUO ◽  
JINGXI YU
Keyword(s):  
Analytic Hierarchy Process ◽  
Wireless Network ◽  
Multiple Attribute Decision Making ◽  
Network Selection ◽  
Heterogeneous Wireless Network ◽  
Network Access ◽  
Selection Algorithm ◽  
Analytic Hierarchy ◽  
Multiple Attribute ◽  
Hierarchy Process

In the research of heterogeneous wireless network access selection algorithm, the method used for setting network attribute parameter weights is usually simple: using one of the subjective or objective weighting methods: Simple Additive Weighting (SAW), Analytic Hierarchy Process (AHP), Entropy, etc. As a result, the network selection results are incomprehensive. In this paper, we propose a new selection algorithm based on the combination of Intuitionistic Normal Fuzzy Analytic Hierarchy Process (INFAHP) and Improved Grey Relation Analysis (IGRA), which has been proved to be a new Multiple Attribute Decision Making (MADM) method based on the combination of subjective and objective weights. This algorithm expresses the semantic importance of the intuitionistic normal fuzzy number and calculates the subjective weight of network attributes using INFAHP while obtaining the objective weight using Coefficient of Variation (CV) at the same time. The final weights are integrated by subjective and objective weights. After that, the candidate networks can be sorted using IGRA. The proposed algorithm can get the more comprehensive weights of the network attributes by integrating the subjective and objective weights. Simulation results show that our algorithm can reduce the ratio of network handovers by 25% in average in the four traffic classes and improve the ping-pong effect effectively, so as to better meet users’ demands for QoS.

scholarly journals A Novel Approach for Classifying MANETs Attacks with a Neutrosophic Intelligent System based on Genetic Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Haitham Elwahsh ◽  
Mona Gamal ◽  
A. A. Salama ◽  
I. M. El-Henawy
Keyword(s):  
Genetic Algorithm ◽  
Ad Hoc ◽  
Intelligent System ◽  
Fitness Function ◽  
Mathematical Formulation ◽  
Training Data ◽  
Data Set ◽  
Novel Approach ◽  
Learning Capabilities ◽  
Hoc Networks

Recently designing an effective intrusion detection systems (IDS) within Mobile Ad Hoc Networks Security (MANETs) becomes a requirement because of the amount of indeterminacy and doubt exist in that environment. Neutrosophic system is a discipline that makes a mathematical formulation for the indeterminacy found in such complex situations. Neutrosophic rules compute with symbols instead of numeric values making a good base for symbolic reasoning. These symbols should be carefully designed as they form the propositions base for the neutrosophic rules (NR) in the IDS. Each attack is determined by membership, nonmembership, and indeterminacy degrees in neutrosophic system. This research proposes a MANETs attack inference by a hybrid framework of Self-Organized Features Maps (SOFM) and the genetic algorithms (GA). The hybrid utilizes the unsupervised learning capabilities of the SOFM to define the MANETs neutrosophic conditional variables. The neutrosophic variables along with the training data set are fed into the genetic algorithm to find the most fit neutrosophic rule set from a number of initial subattacks according to the fitness function. This method is designed to detect unknown attacks in MANETs. The simulation and experimental results are conducted on the KDD-99 network attacks data available in the UCI machine-learning repository for further processing in knowledge discovery. The experiments cleared the feasibility of the proposed hybrid by an average accuracy of 99.3608 % which is more accurate than other IDS found in literature.

scholarly journals QoS-Aware Mobile Service Selection Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Chengwen Zhang ◽  
Lei Zhang ◽  
Guanhua Zhang
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Service Selection ◽  
Mobile Service ◽  
Context Aware ◽  
Selection Algorithm ◽  
Sequence Composition ◽  
Population Evolution ◽  
Encoding Method ◽  
Selection Of

For the problem of mobile service selection, this paper gives a context-aware service selection algorithm based on Genetic Algorithm. In this algorithm, a tree encoding method, a fitness function, and a fitness-better strategy were proposed. The tree encoding mode made Genetic Algorithm support selection of various types of service combinations, for example, sequence composition, concurrence composition, probability composition, and loop composition. According to the encoding method, a fitness function was designed specially. The fitness-better strategy gives the direction of population evolution and avoids the degradation of population fitness. Some experiments analyses show that the provided service selection algorithm can get better service composition.

scholarly journals A new genetic algorithm specifically based on mutation and selection

2007 ◽  
Vol 39 (1) ◽  
pp. 141-161 ◽  
Author(s):  
L. Rigal ◽  
L. Truffet
Keyword(s):  
Genetic Algorithm ◽  
Selection Pressure ◽  
Large Deviation ◽  
Fitness Function ◽  
Critical Value ◽  
Gradient Algorithm ◽  
Selection Algorithm ◽  
Local Maxima ◽  
Selection Operator ◽  
Mutation And Selection

In this paper we propose a new genetic algorithm specifically based on mutation and selection in order to maximize a fitness function. This mutation-selection algorithm behaves as a gradient algorithm which converges to local maxima. In order to obtain convergence to global maxima we propose a new algorithm which is built by randomly perturbing the selection operator of the gradient-like algorithm. The perturbation is controlled by only one parameter: that which allows the selection pressure to be governed. We use the Markov model of the perturbed algorithm to prove its convergence to global maxima. The arguments used in the proofs are based on Freidlin and Wentzell's (1984) theory and large deviation techniques also applied in simulated annealing. Our main results are that (i) when the population size is greater than a critical value, the control of the selection pressure ensures the convergence to the global maxima of the fitness function, and (ii) the convergence also occurs when the population is the smallest possible, i.e. 1.

scholarly journals Network access and spectrum allocation in next-generation multi-heterogeneous networks

2019 ◽  
Vol 15 (8) ◽  
pp. 155014771986614
Author(s):  
Xiaoqing Dong ◽  
Lianglun Cheng ◽  
Gengzhong Zheng ◽  
Tao Wang
Keyword(s):  
Genetic Algorithm ◽  
Transmission Rate ◽  
Standard Form ◽  
Spectrum Allocation ◽  
Allocation Problem ◽  
Network Selection ◽  
Complex Spectrum ◽  
Network Access ◽  
Secondary Users ◽  
Primary Network

In a multi-heterogeneous network with dense deployment and convergence environment, how to efficiently and reasonably allocate idle spectrum resources of the primary network to meet the diversified business demands of secondary users is a difficult problem. In this article, with the goal of maximizing the total transmission rate and minimizing the total cost, a dual-objective optimization mathematical model for network selection and idle spectrum allocation is established in the context of comprehensive consideration of the diversity of spectrum resource attributes and the diversification of secondary users’ business needs. Based on this, two kinds of technical paths to solve the complex network selection and spectrum allocation problem are applied in this article. The first is the simplification method. By preprocessing of objective function, constraint simplification, and standardization, the complex spectrum allocation problem is transformed into a standard form of the 01 programming problem, and the solution is obtained by an improved Hungarian algorithm. Second, an intelligent optimization algorithm named improved non-dominated sorting genetic algorithm II is proposed, which combines the interference constraints of the primary network and the service quality requirements of the secondary users into the objective value evaluation of non-dominated sorting, and corrects the chromosomes that do not meet the constraints. And then makes a decision selection on the optimal solution set to select a compromise solution. Finally, methods proposed in this article are compared with the multi-objective artificial bee colony algorithm through experiments. Experimental results show that the simplified method has higher efficiency, and the improved non-dominated sorting genetic algorithm II can get higher transmission rate, especially the transmission rate–priority strategy.

A Network Selection Algorithm Based on Traffic Flow

2014 ◽  
Vol 571-572 ◽  
pp. 359-363
Author(s):  
Yang Tao ◽  
Yan Li Jiang
Keyword(s):  
Traffic Flow ◽  
Transmission Efficiency ◽  
Network Selection ◽  
Network Access ◽  
Selection Algorithm ◽  
Parallel Transmission ◽  
Optimal Network ◽  
The Times ◽  
Multi Mode ◽  
Individual Service

The existing network selection algorithm generally considering the QoS needs of all services which carried by the terminal. Then selecting an optimal network to access to achieve the overall best of all services, However, for each individual service is not necessarily optimal. To solve the problem above, On the basis of multi-mode terminal and multi- parallel transmission of data, we propose a traffic-based network access selection algorithm, which was designed to realize independent network selection for each traffic flow by considering the network congestion and the transmission of high priority traffic and achieve adaptively network access which making the traffic efficient and reliable transmission, By which each network can be fully utilized. The Simulation results demonstrate that the algorithm can reduce the times of network selection and improve the transmission efficiency and the utilization of network. The results suggest that this kind of network algorithm is effective.

scholarly journals Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm

1995 ◽  
Vol 2 ◽  
pp. 369-409 ◽  
Author(s):  
P. D. Turney
Keyword(s):  
Genetic Algorithm ◽  
Decision Tree ◽  
Real World ◽  
Average Cost ◽  
Fitness Function ◽  
Empirical Evaluation ◽  
The Third ◽  
Decision Tree Induction ◽  
Cost Sensitive Classification ◽  
Better Than

This paper introduces ICET, a new algorithm for cost-sensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. ICET is compared here with three other algorithms for cost-sensitive classification - EG2, CS-ID3, and IDX - and also with C4.5, which classifies without regard to cost. The five algorithms are evaluated empirically on five real-world medical datasets. Three sets of experiments are performed. The first set examines the baseline performance of the five algorithms on the five datasets and establishes that ICET performs significantly better than its competitors. The second set tests the robustness of ICET under a variety of conditions and shows that ICET maintains its advantage. The third set looks at ICET's search in bias space and discovers a way to improve the search.

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