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The goal of QoS aware web service composition (QoS-WSC) is to provide new functionalities and find a best combination of services to meet complex needs of users. QoS of the resulting composite service should be optimized. QoS-WSC is a global multi-objective optimization problem belonging to NP-hard class given the number of available services. Most of existing approaches reduce this problem to a single-objective problem by aggregating different objectives, which leads to a loss of information. An alternative issue is to use Pareto-based approaches. The Pareto-optimal set contains solutions that ensure the best trade-off between conflicting objectives. In this paper, a new multi-objective meta-heuristic bio-inspired Pareto-based approach is presented to address the QoS-WSC, it is based on Elephants Herding Optimization (EHO) algorithm. EHO is characterised by a strategy of dividing and combining the population to sub population (clan) which allows exchange of information between local searches to get a global optimum. However, the application of others evolutionary algorithms to this problem cannot avoids the early stagnancy in a local optimum. In this paper a discrete and multi-objective version of EHO will be presented based on a crossover operator. Compared with SPEA2 (Strength Pareto Evolutionary Algorithm 2) and MOPSO (Multi-Objective Particle Swarm Optimization algorithm), the results of experimental evaluation show that our improvements significantly outperform the existing algorithms in term of Hypervolume, Set Coverage and Spacing metrics.
Chibani Sadouki, Samia 1 ; Tari, Abdelkamel 1
@article{RO_2019__53_2_445_0,
author = {Chibani Sadouki, Samia and Tari, Abdelkamel},
title = {Multi-objective and discrete {Elephants} {Herding} {Optimization} algorithm for {QoS} aware web service composition},
journal = {RAIRO - Operations Research - Recherche Op\'erationnelle},
pages = {445--459},
publisher = {EDP-Sciences},
volume = {53},
number = {2},
year = {2019},
doi = {10.1051/ro/2017049},
zbl = {1436.68388},
language = {en},
url = {http://geodesic.mathdoc.fr/articles/10.1051/ro/2017049/}
}
TY - JOUR AU - Chibani Sadouki, Samia AU - Tari, Abdelkamel TI - Multi-objective and discrete Elephants Herding Optimization algorithm for QoS aware web service composition JO - RAIRO - Operations Research - Recherche Opérationnelle PY - 2019 SP - 445 EP - 459 VL - 53 IS - 2 PB - EDP-Sciences UR - http://geodesic.mathdoc.fr/articles/10.1051/ro/2017049/ DO - 10.1051/ro/2017049 LA - en ID - RO_2019__53_2_445_0 ER -
%0 Journal Article %A Chibani Sadouki, Samia %A Tari, Abdelkamel %T Multi-objective and discrete Elephants Herding Optimization algorithm for QoS aware web service composition %J RAIRO - Operations Research - Recherche Opérationnelle %D 2019 %P 445-459 %V 53 %N 2 %I EDP-Sciences %U http://geodesic.mathdoc.fr/articles/10.1051/ro/2017049/ %R 10.1051/ro/2017049 %G en %F RO_2019__53_2_445_0
Chibani Sadouki, Samia; Tari, Abdelkamel. Multi-objective and discrete Elephants Herding Optimization algorithm for QoS aware web service composition. RAIRO - Operations Research - Recherche Opérationnelle, Tome 53 (2019) no. 2, pp. 445-459. doi : 10.1051/ro/2017049. http://geodesic.mathdoc.fr/articles/10.1051/ro/2017049/
[1] and , QoS-based discovery and ranking of web services. In Proceedings of 16th International Conference on Computer Communications and Networks, ICCCN (2007) 529–534.
[2] , and , Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation). Springer; 2nd Edition (2007). | Zbl
[3] and , MOPSO: a proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation, CEC ’02 (2002) 1051–1056.
[4] , , and , An approach for QoS-aware service composition based on genetic algorithms. In GECCO ‘05 Proceedings of the 2002 Congress on Evolutionary Computation (2005) 1069–1075.
[5] , and , Optimizing dynamic web service component composition by using evolutionary algorithms. In IEEE International Conference on Web Intelligence (2005) 708–711.
[6] , , and , Comparative analysis of multi-objective evolutionary algorithms for QoS-aware web service composition. Inter. J. Appl. Soft Comput. 39 (2016) 124–139.
[7] and , QoS based web services composition using ant colony optimization: mobile agent approach. Inter. J. Adv. Res. Comput. Commun. Eng. 1 (2012) 519–527.
[8] and , A novel web service composition using ant colony optimization with agent based approach. Inter. J. Emerging Technologies and Innovative Res. 2 (2015) 1685–1688.
[9] and , QoS-based selection of services: The implementation of a genetic algorithm. In Commun. Distributed Syst. (KiVS), ITG-GI Confer. (2007) 1–12.
[10] , Fault tolerant design using single and multicriteria genetic algorithm optimization. In Technical report, DTIC Document (1995).
[11] , , and , Consistencies and contradictions of performance metrics in multiobjective optimization. IEEE Trans. Cybernetics 44 (2014) 2391–2404.
[12] and , Les Web services Techniques, dmarches et outils XML, WSDL, SOAP, UDDI, Rosetta, UML. Edition Dunod (2003).
[13] , and , 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) 131–139.
[14] , and , Web service composition: A survey of techniques and tools. ACM Computing Surveys 48 (2015) 33, 41.
[15] , , and , Applying multi-objective evolutionary algorithms to QoS-aware web service composition. In 6th International Conference on Advanced Data Mining and Applications (2010) 270–281.
[16] , , , and , A multi-objective service selection algorithm for service composition. In 19th Asia-Pacific Conference on Communications (APCC), Bali Indonesia (2013) 75–80.
[17] , and , Performance metrics in multi-objective optimization. In IEEE Latin American Computing Conference (CLEI) (2015) 1–11.
[18] and , Survey of multi-objective optimization methods for engineering. Inter. J. Structural Multidisciplinary Optimiz. 26 (2004) 369–395. | Zbl
[19] , and , Elephant herding optimization. In 3rd International Symposium on Computational and Business Intelligence (2015) 1–5.
[20] , , and , A new metaheuristic optimisation algorithm motivated by elephant herding behaviour. Inter. J. Bio-Inspired Comput. 8 (2016) 394–409.
[21] , , and , An improved particle swarm optimization algorithm for QoS-aware web service selection in service oriented communication. Inter. J. Comput. Intell. Syst. 3 (2010) 18–30.
[22] and , Transactional and QoS-aware dynamic service composition based on ant colony optimization. Future Generation Comput. Syst. 29 (2013) 1112–1119.
[23] and , QoS-aware service composition using NSGA-II1. In ICIS ’09 Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human (2009) 358–363.
[24] , , and , Multi-objective approach for energy-aware workflow scheduling in cloud computing environments. The Scientific World Journal 2013 (2013) 350934.
[25] and , A survey on QoS-aware web service composition. In Third Inter. Confer. Multimedia Information Networking and Security (MINES) (2011) 283–287.
[26] , , , , and , QoS-aware middleware for web services composition. IEEE Trans. Software Eng. 3 (2004) 311–327.
[27] , and , Comparison of multi-objective evolutionary algorithms: Empirical results. J. Evolutionary Comput. 8 (2000) 173–195.
[28] , , and , Web service dynamic composition based on decomposition of global QoS constraints. Inter. J. Adv. Manufacturing Technology 69 (2013) 2247–2260.
[29] , and SPEA2: improving the strength pareto evolutionary algorithm for multi-objective optimization. In Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems. Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, edited by , , , , . International Center for Numerical Methods in Engineering (2001) 95–100.
[30] and , Multi-objective evolutionary algorithms: A comparative case study and the strength pareto approach. IEEE Trans. Evolutionary Comput. 3 (1999) 257–271.
[31] and , Multi-objective optimization using evolutionary algorithms- A comparative case study. In Parallel Problem Solving from Nature. Edited by , , , . In Vol. 1798 of Lecture Notes in Computer Science. Springer, Berlin (1998).
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