A Pattern and Summarization Based Optimization Algorithm to QoS-Aware Web Service Composition Selection | ||
| Journal of AI and Data Mining | ||
| دوره 13، شماره 2، تیر 2025، صفحه 183-206 اصل مقاله (1.91 M) | ||
| نوع مقاله: Original/Review Paper | ||
| شناسه دیجیتال (DOI): 10.22044/jadm.2025.15435.2654 | ||
| نویسندگان | ||
| Seyed Morteza Babamir* ؛ Narges Zahiri | ||
| Department of Software Engineering, University of Kashan, Kashan, Iran. | ||
| چکیده | ||
| Web service composition represents a graph of interacting services designed to fulfill user requirements, where each node denotes a service, and each edge represents an interaction between two services. A few candidates with different quality attributes exist on the web for conducting each web service. Consequently, numerous compositions with identical functionality but differing quality attributes can be formed, making the near-optimal composition selection an NP-hard problem. This paper proposes a tool-supported Evolutionary Optimization Algorithm (EOA) for near-optimal composition selection. The proposed EOA is a Discretized and Extended Gray Wolf Optimization (DEGWO) algorithm. This approach first discretizes the continuous solution space and then extends the functionality of GWO to identify global near-optimal solutions while accelerating solution convergence. DEGWO was evaluated in comparison with other related methods in terms of metrics. Experimental results showed DEGWO achieved average improvements of 8%, 39%, and 5% in terms of availability, 36%, 43%, and 30% in terms of response time, and 65%, 53%, and 51% in terms of cost compared to the three leading algorithms, RDGWO+GA, HGWO, and SFLAGA, respectively. | ||
| کلیدواژهها | ||
| Web service composition؛ Quality based composition selection؛ Service interaction pattern؛ Grey Wolf Optimization | ||
| مراجع | ||
|
[1] N. Kashyap, A. C. Kumari, and R. Chhikara, “Service Composition in IoT using Genetic algorithm and Particle swarm optimization,” Open Computer Science, vol. 10, no. 1, pp. 56–64, 2020.
[2] P. Asghari, A. M. Rahmani, and H. H. S. Javadi, “Privacy-aware cloud service composition based on QoS optimization in Internet of Things,” Journal of Ambient Intelligence and Humanized Computing, pp. 1–26, 2020.
[3] S. Chattopadhyay, A. Banerjee, and N. Banerjee, “A fast and scalable mechanism for web service composition,” ACM Transactions on the Web (TWEB), vol. 11, no. 4, pp. 1–36, 2017.
[4] F. B. Vernadat, "Interoperability and Standards for Automation," in Springer Handbook of Automation, Springer, 2023, pp. 729–752.
[5] N. Antonyuk, M. Medykovskyy, L. Chyrun, M. Dverii, O. Oborska, M. Krylyshyn, A. Vysotsky, N. Tsiura, and O. Naum, "Online tourism system development for searching and planning trips with user’s requirements," in Proc. of the 2020 International Conference on Information Technology and Tourism Development (ICITD 2020), Lviv, Ukraine, 2020, pp. 831–863.
[6] V. Gabrel, M. Manouvrier, K. Moreau, and C. Murat, “QoS-aware automatic syntactic service composition problem: Complexity and resolution,” Future Generation Computer Systems, vol. 80, pp. 311–321, 2018.
[7] P. Asghari, A. M. Rahmani, and H. H. S. Javadi, “Service composition approaches in IoT: A systematic review,” Journal of Network and Computer Applications, vol. 120, pp. 61–77, 2018.
[8] A. Ramírez, J. A. Parejo, J. R. Romero, S. Segura, and A. Ruiz-Cortés, “Evolutionary composition of QoS-aware web services: a many-objective perspective,” Expert Systems with Applications, vol. 72, pp. 357–370, 2017.
[9] M. Dumas, L. García-Bañuelos, A. Polyvyanyy, Y. Yang, and L. Zhang, "Aggregate quality of service computation for composite services," in Proc. of the 2017 International Conference on Service-Oriented Computing (ICSOC 2017), Malaga, Spain, 2017, pp. 213–227.
[10] H. Zheng, W. Zhao, J. Yang, and A. Bouguettaya, “QoS Analysis for Web Service Compositions with Complex Structures,” IEEE Transactions on Services Computing, vol. 6, no. 3, pp. 373–386, 2013.
[11] S. Asghari and N. J. Navimipour, “Nature inspired meta-heuristic algorithms for solving the service composition problem in the cloud environments,” International Journal of Communication Systems, vol. 31, no. 12, Art. no. e3708, 2018. [12] M. AllamehAmiri, V. Derhami, and M. Ghasemzadeh, "QoS-based web service composition based on genetic algorithm," J. AI Data Min., vol. 1, no. 2, pp. 63–73, 2013.
[13] H.-F. Li, L. Zhao, B.-H. Zhang, and J.-Q. Li, "Service matching and composition considering correlations among cloud services," in Proc. of the 2018 IEEE International Conference on Web Services (ICWS 2018), San Francisco, CA, USA, 2018, pp. 509–514.
[14] M. Eusuff, K. Lansey, and F. Pasha, “Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization,” Engineering Optimization, vol. 38, no. 2, pp. 129–154, 2006.
[15] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey wolf optimizer,” Advances in Engineering Software, vol. 69, pp. 46–61, 2014.
[16] H. Bouzary and F. Frank Chen, “A hybrid grey wolf optimizer algorithm with evolutionary operators for optimal QoS-aware service composition and optimal selection in cloud manufacturing,” The International Journal of Advanced Manufacturing Technology, vol. 101, pp. 2771–2784, 2019.
[17] J. Zhou and X. Yao, “A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition,” International Journal of Production Research, vol. 55, no. 16, pp. 4765–4784, 2017.
[18] F. Seghir and A. Khababa, “A hybrid approach using genetic and fruit fly optimization algorithms for QoS-aware cloud service composition,” Journal of Intelligent Manufacturing, vol. 29, pp. 1773–1792, 2018.
[19] G. Komaki and V. Kayvanfar, “Grey Wolf Optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time,” Journal of Computational Science, vol. 8, pp. 109–120, 2015.
[20] X. Song, L. Tang, S. Zhao, X. Zhang, L. Li, J. Huang, and W. Cai, “Grey Wolf Optimizer for parameter estimation in surface waves,” Soil Dynamics and Earthquake Engineering, vol. 75, pp. 147–157, 2015.
[21] M. Chandra, A. Agrawal, A. Kishor, and R. Niyogi, "Web service selection with global constraints using modified gray wolf optimizer," in Proc. of the 2019 IEEE International Conference on Web Services (ICWS 2019), Milan, Italy, 2019, pp. 1989–1994.
[22] S. Gohain and A. Paul, "Web service composition using PSO—ACO," in Proc. of the 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI 2016), Jaipur, India, 2016, pp. 1–5.
[23] M. Karimi and S. M. Babamir, “QoS-aware web service composition using Gray Wolf Optimizer,” International Journal of Information and Communication Technology Research, vol. 9, no. 1, pp. 9–16, 2017.
[24] Y. Huo, P. Qiu, J. Zhai, D. Fan, and H. Peng, “Multi-objective service composition model based on cost-effective optimization,” Applied Intelligence, vol. 48, pp. 651–669, 2018.
[25] S. C. Sadouki and A. Tari, “Multi-objective and discrete elephants herding optimization algorithm for QoS aware web service composition,” RAIRO-Operations Research, vol. 53, no. 2, pp. 445–459, 2019.
[26] Y. Yang, B. Yang, S. Wang, T. Jin, and S. Li, “An enhanced multi-objective grey wolf optimizer for service composition in cloud manufacturing,” Applied Soft Computing, vol. 87, Art. no. 106003, 2020.
[27] A. K. Sangaiah, G.-B. Bian, S. M. Bozorgi, M. Y. Suraki, A. A. R. Hosseinabadi, and M. B. Shareh, “A novel quality-of-service-aware web services composition using biogeography-based optimization algorithm,” Soft Computing, vol. 24, pp. 8125–8137, 2020.
[28] P. Thangaraj and P. Balasubramanie, “Meta heuristic QoS based service composition for service computing,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 5619–5625, 2021.
[29] F. Dahan, W. Binsaeedan, M. Altaf, M. S. Al-Asaly, and M. M. Hassan, “An efficient hybrid evolutionary algorithm for QoS-Aware cloud service composition problem,” IEEE Access, vol. 9, pp. 95208–95217, 2021.
[30] Y. Azouz and D. Boughaci, “Multi-objective memetic approach for the optimal web services composition,” Expert Systems, Art. no. e13084, 2022.
[31] F. Dahan and A. Alwabel, “Artificial Bee Colony with Cuckoo Search for Solving Service Composition,” Intelligent Automation & Soft Computing, vol. 35, no. 3, 2023. | ||
|
آمار تعداد مشاهده مقاله: 237 تعداد دریافت فایل اصل مقاله: 220 |
||