Abstract
Web service composition is considered as the hottest and potential research area in the domain of Service Oriented Architecture since the users focus on Quality of Service (QoS) and transaction properties included in the integration of services. Moreover, the potential quality of modularity and reusability features of web services has wide open the feasible options of integrating diversified function oriented services together with the better optimization capability. Hence, a meta-heuristic approach-based web service composition scheme is essential for facilitating superior and comprehensive quality during the process of integrating services. In this paper, An Integrated Probability Multi-search and Solution Acceptance Rule-based Artificial Bee Colony Optimization Scheme (IPM-SAR-ABCOS) is proposed for optimizing the process of service compositions derived using transaction and QoS characteristics of services. This proposed IPM-SAR-ABCOS is efficient in determining the optimal path that exists between the source and sink vertex of the workflow inspired directed acyclic graph that aids in predominant service composition. The proposed IPM-SAR-ABCOS uses the rules of acceptance and multi-search probabilistic parameter for addressing the process of global optimization in service composition. The experimental analysis of the proposed IPM-SAR-ABCOS inferred that its response time, accuracy and recall value is enhanced by 24%, 22% and 19% excellent to the ABC-based meta-heuristic service composition techniques considered for analysis.













Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Alexander T, Kirubakaran E (2014) Optimal QoS based web service choreography using ant colony optimization. Int J Comput Appl 102(11):39–46
Blanco E, Cardinale Y, Vidal M, El Haddad J, Manouvrier M, Rukoz M (2012a) A transactional-QoS driven approach for web service composition. Resour Discov 2(1):23–42
Blanco E, Cardinale Y, Vidal ME (2012b) Experiences of sampling-based approaches for estimating QoS parameters in the web service composition problem. Int J Web Grid Serv 8(1):1
Cardinale Y, Haddad JE, Manouvrier M, Rukoz M (2011) CPN-TWS: a coloured petri-net approach for transactional-QoS driven web service composition. Int J Web Grid Serv 7(1):91
Chattopadhyay S, Banerjee A (2017) QoS constrained large scale web service composition using abstraction refinement. IEEE Trans Serv Comput 2(1):1-1
Chen L, Ha W (2018) Reliability prediction and QoS selection for web service composition. Int J Comput Sci Eng 16(2):202
Deng S, Du Y (2013) Web service composition approach based on service cluster and QoS. J Comput Appl 33(8):2167–2170
El Hadad J, Manouvrier M, Rukoz M (2010) TQoS: transactional and QoS-aware selection algorithm for automatic web service composition. IEEE Trans Serv Comput 3(1):73–85
Gupta IK, Kumar J, Rai P (2015) Optimization to quality-of-service-driven web service composition using a modified genetic algorithm. In: 2015 International conference on computer, communication and control (IC4) vol 2, no 1, pp 54–65
Hao L (2011) An improved genetic algorithm-based web service composition. Adv Mater Res 225–226(1):307–310
He L, Zhao F, Rao J (2013a) Web service composition method based on community service chain. J Comput Appl 33(1):250–253
He J, Chen L, Wang X, Li Y (2013b) Web service composition optimization based on improved artificial bee colony algorithm. J Netw 8(9):34–46
Huang L, Zhang X, Huang Y, Wang G, Wang R (2011) A QoS optimization for intelligent and dynamic web service composition based on improved PSO algorithm. In: 2011 Second international conference on networking and distributed computing vol 2, no 1, pp 67–76
Huo L, Wang Z (2016) Service composition instantiation based on cross-modified artificial Bee Colony algorithm. China Commun 13(10):233–244
Jatoth C, Gangadharan GR (2015) Fitness metrics for QoS-aware web service composition using metaheuristics. Intel Decis Technol 2(1):267–277
Jatoth C, Gangadharan G, Buyya R (2017) Computational intelligence based QoS-aware web service composition: a systematic literature review. IEEE Trans Serv Comput 10(3):475–492
Li J, Yuan SF (2016) Domain quality-driven logistics web service optimal composition based on culture artificial bee colony algorithm. J Intel Fuzzy Syst 31(4):2383–2391
Liu Z, Xu X (2014) S-ABC—a service-oriented artificial bee colony algorithm for global optimal services selection in concurrent requests environment. In: 2014 IEEE international conference on web services vol 8, no 9, pp 34–46
Liu R, Wang Z, Xu X (2014) Parameter tuning for ABC-based service composition with end-to-end QoS constraints. In: 2014 IEEE international conference on web services vol 1, no 1, pp 23–34
Ma H, Zhou X (2013) Service composition optimization approach based on the affection ant colony algorithm. J Comput Appl 32(12):3347–3352
Omer AM, Schill A (2009) Dependency based automatic service composition using directed graph. In: 2009 Fifth international conference on next generation web services practices vol 1, no 2, pp 23–32
Wen T, Sheng G, Guo Q, Li Y (2014) Web service composition based on modified particle swarm optimization. Chin J Comput 36(5):1031–1046
Xu B, Luo S (2012) Efficient composition of semantic web services with end-to-end QoS optimization. Semantic Web Serv 1(1):345–355
Zhang G, Chen L, Ha W (2012) Service Selection of ensuring transactional reliability and QoS for web service composition. Math Prob Eng 2012(1):1–15
Zhao C, Wang J, Qin J, Zhang W (2015) A hybrid algorithm combining ant colony algorithm and genetic algorithm for dynamic web service composition. Open Cybern Syst J 8(1):146–154
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Arunachalam, N., Amuthan, A. Integrated probability multi-search and solution acceptance rule-based artificial bee colony optimization scheme for web service composition. Nat Comput 20, 23–38 (2021). https://doi.org/10.1007/s11047-019-09753-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11047-019-09753-7