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.

Service Selection with Uncertain Context Information

2012 ◽  
pp. 192-215 ◽  
Author(s):  
Yves Vanrompay ◽  
Manuele Kirsch-Pinheiro ◽  
Yolande Berbers
Keyword(s):  
Real Life ◽  
Similarity Measures ◽  
Service Selection ◽  
Context Information ◽  
Context Aware ◽  
Functional Requirements ◽  
Selection Algorithm ◽  
Service Description ◽  
Inexact Matching ◽  
Service Ranking

The current evolution of Service-Oriented Computing in ubiquitous systems is leading to the development of context-aware services. Context-aware services are services of which the description is enriched with context information related to non-functional requirements, describing the service execution environment or its adaptation capabilities. This information is often used for discovery and adaptation purposes. However, in real-life systems, context information is naturally dynamic, uncertain, and incomplete, which represents an important issue when comparing the service description with user requirements. Uncertainty of context information may lead to an inexact match between provided and required service capabilities, and consequently to the non-selection of services. In this chapter, we focus on how to handle uncertain and incomplete context information for service selection. We consider this issue by presenting a service ranking and selection algorithm, inspired by graph-based matching algorithms. This graph-based service selection algorithm compares contextual service descriptions using similarity measures that allow inexact matching. The service description and non-functional requirements are compared using two kinds of similarity measures: local measures, which compare individually required and provided properties, and global measures, which take into account the context description as a whole.

Layout optimization of a military operations center using a genetic algorithm

2020 ◽  
pp. 154851292093707
Author(s):  
Wenbi Wang
Keyword(s):  
Genetic Algorithm ◽  
Human Factors ◽  
Fitness Function ◽  
Military Operations ◽  
Abstract Representation ◽  
Genetic Encoding ◽  
Encoding Method ◽  
Elitist Strategy ◽  
Operations Center

A genetic algorithm was developed to support the spatial layout design of military operations centers. Based on an abstract representation of the workplace, the algorithm uses a textual string as the genetic encoding method, two genetic operations (i.e., selection and swap) for simulating an evolution process, a fitness function that reflects a human factors characterization of workplace layout requirements, and an elitist strategy for improving its search efficiency. The effectiveness of the algorithm was demonstrated in the design of a mid-sized operations center that involved a team of 68 operators. This algorithm expands the human factors practitioners’ toolkit and enhances their ability to examine layout options of complex workplaces using modeling and simulation.

Optimization of Clustering in Wireless Sensor Networks Using Genetic Algorithm

2020 ◽  
pp. 822-836
Author(s):  
Pritee Parwekar ◽  
Sireesha Rodda
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Network ◽  
Sensor Node ◽  
Fitness Function ◽  
Wireless Sensor ◽  
Sleep Mode ◽  
Communication Distance ◽  
Selection Of

The energy of a sensor node is a major factor for life of a network in wireless sensor network. The depletion of the sensor energy is dependent on the communication range from the sink. Clustering is mainly used to prolong the life of a network with energy consumption. This paper proposes optimization of clustering using genetic algorithm which will help to minimize the communication distance. The cluster overhead and the active and sleep mode of a sensor is also considered while calculating the fitness function to form the cluster. This approach helps to prolong the network life of sensor network. The proposed work is tested for different number of nodes and is helping to find the correct solution for the selection of cluster heads.

Arrangement Optimization of Instruments Based on Genetic Algorithm

2010 ◽  
Vol 97-101 ◽  
pp. 3622-3626
Author(s):  
Sheng Yuan Yan ◽  
Kun Yu ◽  
Zhi Jian Zhang ◽  
Min Jun Peng
Keyword(s):  
Genetic Algorithm ◽  
Fitness Function ◽  
Solution Space ◽  
Optimization Method ◽  
Human Machine Interaction ◽  
System A ◽  
Frequency Of Use ◽  
Search Speed ◽  
Machine Interface ◽  
Encoding Method

The instruments arrangement of human-machine interface can directly influence the operation and efficiency of human-machine interaction in system. A novel instruments arrangement optimization method based on genetic algorithm was proposed. The biology heredity and evolution mode of genetic algorithm was used to search for the optimal or satisfying arrangement solution. Fitness function was constructed based on the principles of importance, frequency of use, relevance and operational sequence. Literal permutation encoding method was applied to represent the instruments arrangement. The optimization preserving strategy was used to enhance the search speed and to enlarge the width and depth of solution space. Finally, a case of instruments arrangement optimization proves that the arrangement optimization method is effective.

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 An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Saeid Jafarzadeh Ghoushchi ◽  
Ramin Ranjbarzadeh ◽  
Amir Hussein Dadkhah ◽  
Yaghoub Pourasad ◽  
Malika Bendechache
Keyword(s):  
Genetic Algorithm ◽  
Diagnostic Method ◽  
Fitness Function ◽  
Jaccard Index ◽  
Diabetic Patients ◽  
Growth Region ◽  
New Approach ◽  
Fuzzy C Means ◽  
Extended Approach ◽  
Selection Of

The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

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.

Optimization of Clustering in Wireless Sensor Networks Using Genetic Algorithm

2017 ◽  
Vol 8 (4) ◽  
pp. 84-98 ◽  
Author(s):  
Pritee Parwekar ◽  
Sireesha Rodda
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Network ◽  
Sensor Node ◽  
Fitness Function ◽  
Wireless Sensor ◽  
Sleep Mode ◽  
Communication Distance ◽  
Selection Of

The energy of a sensor node is a major factor for life of a network in wireless sensor network. The depletion of the sensor energy is dependent on the communication range from the sink. Clustering is mainly used to prolong the life of a network with energy consumption. This paper proposes optimization of clustering using genetic algorithm which will help to minimize the communication distance. The cluster overhead and the active and sleep mode of a sensor is also considered while calculating the fitness function to form the cluster. This approach helps to prolong the network life of sensor network. The proposed work is tested for different number of nodes and is helping to find the correct solution for the selection of cluster heads.

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