Published August 1, 2017
| Version 10007974
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Elephant Herding Optimization for Service Selection in QoS-Aware Web Service Composition
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Description
Web service composition combines available services
to provide new functionality. Given the number of available
services with similar functionalities and different non functional
aspects (QoS), the problem of finding a QoS-optimal web service
composition is considered as an optimization problem belonging to
NP-hard class. Thus, an optimal solution cannot be found by exact
algorithms within a reasonable time. In this paper, a meta-heuristic
bio-inspired is presented to address the QoS aware web service
composition; it is based on Elephant Herding Optimization (EHO)
algorithm, which is inspired by the herding behavior of elephant
group. EHO is characterized by a process of dividing and combining
the population to sub populations (clan); this process allows the
exchange of information between local searches to move toward
a global optimum. However, with Applying others evolutionary
algorithms the problem of early stagnancy in a local optimum
cannot be avoided. Compared with PSO, the results of experimental
evaluation show that our proposition significantly outperforms the
existing algorithm with better performance of the fitness value and a
fast convergence.
to provide new functionality. Given the number of available
services with similar functionalities and different non functional
aspects (QoS), the problem of finding a QoS-optimal web service
composition is considered as an optimization problem belonging to
NP-hard class. Thus, an optimal solution cannot be found by exact
algorithms within a reasonable time. In this paper, a meta-heuristic
bio-inspired is presented to address the QoS aware web service
composition; it is based on Elephant Herding Optimization (EHO)
algorithm, which is inspired by the herding behavior of elephant
group. EHO is characterized by a process of dividing and combining
the population to sub populations (clan); this process allows the
exchange of information between local searches to move toward
a global optimum. However, with Applying others evolutionary
algorithms the problem of early stagnancy in a local optimum
cannot be avoided. Compared with PSO, the results of experimental
evaluation show that our proposition significantly outperforms the
existing algorithm with better performance of the fitness value and a
fast convergence.
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References
- L.Zeng, B. Benatallah, A.H.H. Ngu, M. Dumas, J. Kalagnanam, and H. Chang. Qos-aware middleware for web services composition. IEEE Trans. Softw. Eng, 2004.
- R. Marler and J. Arora. Survey of multi-objective optimization methods for engineering. International journal of Struct. Multidiscipl. Optim., 2004.
- A. Strunk, QoS-aware service composition: a survey, in: Proceedings of the 2010 Eighth IEEE European Conference on Web Services, ECOWS 10, IEEE Computer Society, Washington, DC, USA, 2010, pp. 6774.
- G.-G. Wang, S. Deb, and L. Coelho. Elephant herding optimization. In 3rd International Symposium on Computational and Business Intelligence, 2015.
- S. Yulu and C. Xi. A survey on qos-aware web service composition. In Third International Conference on Multimedia Information Networking and Security (MINES), 2011.
- G. Canfora, M. Penta, R. Espositio, and M. L. Villani. An approach for qos-aware service composition based on genetic algorithms. In conference on Genetic and eVolutionary computation GECCO 05, 2005.
- M. C. Jaeger and G. Muehl. Qos-based selection of services: The implementation of a genetic algorithm. In Communication in Distributed Systems (KiVS), 2007 ITG-GI Conference, 2007.
- W.C. Chang, C.S. Wu, and C. Chang. Optimizing dynamic web service component composition by using evolutionary algorithms. In IEEE International Conference on Web Intelligence, 2005.
- S.R.Dhore and M. Kharat. Qos based web services composition using ant colony optimization: mobile agent approach. International Journal of Advanced Research in Computer and Communication Engineering, 2012. [10] Q. Wu and Q. Zhu. Transactional and qos-aware dynamic service composition based on ant colony optimization. Future Generation Comp. Syst, 2013. [11] J. Gatha Jayjit and V. Gohel Piyush. Optimization with agent based approach. International Journal of Emerging Technologies and Innovative Research, 2015. [12] W. Wang, Q. Sun, X. Zhao, and F. Yang. An improved particle swarm optimization algorithm for qos -aware web service selection in service oriented communication. International Journal of Computational Intelligence Systems, 2010. [13] S. Kalepu, S. Krishnaswamy, and S. W. Loke. Verity: A qos metric for selecting web services and providers. In Proceedings of the Fourth International Conference on Web Information Systems Engineering Workshops (WISEW03), 2004. [14] A. L. Lemos, F. Daniel, and B. Benatallah. Web service composition: A survey of techniques and tools, acm computing surveys. ACM Computing Surveys, 2015. [15] E. Al-Masri and Q.H. Mahmoud, Qos-based discovery and ranking of web services. In Proceedings of 16th International Conference on Computer Communications and Networks, ICCCN, 2007.