An improved Particle Swarm Optimization Algorithm for QoS-aware Web Service Selection in Service Oriented Communication

Wang, Wenbin; Sun, Qibo; Zhao, Xinchao; Yang, Fangchun

doi:10.1080/18756891.2010.9727750

Cited by 51 publications

(33 citation statements)

References 13 publications

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“…If any one of the node contains no parent, then the table provides very narrow probabilities of that node. Based on the Bayesian probability conclusions, the conditional probability table (CPT) can be calculated from the statistical data and cultivate into the structure of the network [29]. The final probability value will be used through setting up the threshold of confidence as the representation for the cataloging verdict.…”

Section: Bayesian Network To Optimize the Prediction Of Qos Value mentioning

confidence: 99%

Bayesian Network with CSA to optimize the selection of QoS- aware Web service

Chandrasekaran¹,

Srinivasan²,

Parthiban³

2018

IJCSE

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Abstract-The Quality of Service (QoS) is a crucial factor for selecting a web service. It is one among the major multi objective optimization problem so the selection of QoS aware web service is always crucial. The predicted values of the QoS are likely to fluctuate due to the unpredictable network connection and user environment. In this paper, the Bayesian Network (BN) with Cuckoo Search Algorithm (CSA) is proposed for optimizing the selection of QoS-aware web service. The proposed BN-CSA model approach, takes into account the response time, reliability and accuracy for the selection of QoS-aware web service. Data for training purpose is developed and to calculate the best trained data by utilizing the CSA, is a optimization algorithm which is based on meta Heuristic and it is suitable for solving optimization problems. The parameters of the cuckoo search are kept constant and the steps of CSA are selection, creation and updating. Problems with multi-objective optimization are solved with the advantage of fitness function. The proposed optimization process is built in JAVA platform and its performance is examined and compared with other existing techniques, the proposed BN-CSA outperforms with fast convergence and less execution time to find the most optimized fitness value.

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Section: Bayesian Network To Optimize the Prediction Of Qos Value mentioning

confidence: 99%

Bayesian Network with CSA to optimize the selection of QoS- aware Web service

Chandrasekaran¹,

Srinivasan²,

Parthiban³

2018

IJCSE

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“…The authors of [55] designed a non-uniform mutation strategy, an adaptive weight adjustment strategy, and a local best first strategy for a PSO algorithm. These strategies were used to enhance the population diversity and to improve the convergence rate.…”

Section: ) Initialization the Swarm Population Is Formedmentioning

confidence: 99%

A Systematic Review of Bio-Inspired Service Concretization

Wang

Shen

2017

IEEE Trans. Serv. Comput.

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In service oriented computing, Web service selection is an important part of Web service composition. The Web service composition is achieved by solving the Web service concretization problem. The literature presents two types of Web service concretization approaches: local optimization approaches and global optimization approaches. There are three types of algorithmic methods in the global optimization approaches: optimal methods, sub-optimal methods, and soft constraints-based methods. The bio-inspired algorithms are sub-optimal methods. This paper will firstly present a hierarchical taxonomy of Web service concretization approaches. Then we conduct a systematic review on the current research of Web service concretization based on three bio-inspired algorithms, namely, ant colony optimization algorithms, genetic algorithms, and particle swarm optimization algorithms. Based on the findings from the systematic review, this paper also discusses the underlying applications of bio-inspired algorithms to the data-intensive service concretization problems. Abstract-In service oriented computing, Web service selection is an important part of Web service composition. The Web service composition is achieved by solving the Web service concretization problem. The literature presents two types of Web service concretization approaches: local optimization approaches and global optimization approaches. There are three types of algorithmic methods in the global optimization approaches: optimal methods, sub-optimal methods, and soft constraints-based methods. The bioinspired algorithms are sub-optimal methods. This paper will first present a hierarchical taxonomy of web service concretization approaches. Then we conduct a systematic review on the current research of Web service concretization based on three bio-inspired algorithms, namely, ant colony optimization algorithms, genetic algorithms, and particle swarm optimization algorithms. Based on the findings from the systematic review, this paper also discusses the underlying applications of bio-inspired algorithms to the dataintensive service concretization problems.

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“…Suggesting that all services follow a serial execution path, the overall Response time, based on [18,19], is:…”

Section: Case Studymentioning

confidence: 99%