scholarly journals Improved Hunting Search Algorithm for Web Service Composition Optimization

2021 ◽  
Vol 1994 (1) ◽  
pp. 012033
Author(s):  
Q J Wang ◽  
Q L Zhang ◽  
J T Li ◽  
X Y Song ◽  
Z Y Liu
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Eckwijai Maythaisong ◽  
Wararat Songpan

Web service composition is a method of developing a new web service from an existing one based on business goals. Web services selected for composition should provide accurate operational results and reliable applications. However, most alternative service providers have not yet fulfilled users’ needs in terms of services and processes. Service providers, in fact, have focused on enhancing nonfunctional attributes, such as efficiencies of time, cost, and availability, which still face limitations. Furthermore, it remains advantageous to compose services and suitably plan them around business plans. Thus, this study introduces hybrid testing using a combination of the functional and nonfunctional testing approaches. The former was used to design a test case through the equivalence class partitioning technique, and the latter was used to select suitable services for the test results. We find defects and appropriate solutions for combining services based on business requirements. The mutation-based harmony search (MBHS) algorithm is proposed to select web services and to compose with minimum defects. The results of this study reveal that MBHS can support a combination of various services more efficiently and dramatically than other metaheuristic methodologies. Additionally, it helps find appropriate solutions to compose services based on business plans.


2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Xing Guo ◽  
Shanshan Chen ◽  
Yiwen Zhang ◽  
Wei Li

Web service composition is one of the core technologies of realizing service-oriented computing. Web service composition satisfies the requirements of users to form new value-added services by composing existing services. As Cloud Computing develops, the emergence of Web services with different quality yet similar functionality has brought new challenges to service composition optimization problem. How to solve large-scale service composition in the Cloud Computing environment has become an urgent problem. To tackle this issue, this paper proposes a parallel optimization approach based on Spark distributed environment. Firstly, the parallel covering algorithm is used to cluster the Web services. Next, the multiple clustering centers obtained are used as the starting point of the particles to improve the diversity of the initial population. Then, according to the parallel data coding rules of resilient distributed dataset (RDD), the large-scale combination service is generated with the proposed algorithm named Spark Particle Swarm Optimization Algorithm (SPSO). Finally, the usage of particle elite selection strategy removes the inert particles to optimize the performance of the combination of service selection. This paper adopts real data set WS-Dream to prove the validity of the proposed method with a large number of experimental results.


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