An Enhanced Ant Colony Optimization Based Algorithm to Solve QoS-Aware Web Service Composition

Web Service Composition (WSC) can be defined as the problem of consolidating the services regarding the complex user requirements. These requirements can be represented as a workflow. This workflow consists of a set of abstract task sequence where each sub-task represents a definition of some user requirements. In this work, we propose a more efficient neighboring selection process and multi-pheromone distribution method named Enhanced Flying Ant Colony Optimization (EFACO) to solve this problem. The WSC problem has a challenging issue, where the optimization algorithms search the best combination of web services to achieve the functionality of the workflow’s tasks. We aim to improve the computation complexity of the Flying Ant Colony Optimization (FACO) algorithm by introducing three different enhancements. We analyze the performance of EFACO against six of existing algorithms and present a summary of our conclusions.

[1]  Eyhab Al-Masri,et al.  QoS-based Discovery and Ranking of Web Services , 2007, 2007 16th International Conference on Computer Communications and Networks.

[2]  Tao Yu,et al.  Service selection algorithms for Web services with end-to-end QoS constraints , 2004, Proceedings. IEEE International Conference on e-Commerce Technology, 2004. CEC 2004..

[3]  Abderrahim Ait Wakrime,et al.  Cloud service composition using minimal unsatisfiability and genetic algorithm , 2020, Concurr. Comput. Pract. Exp..

[4]  Tao Yu,et al.  Efficient algorithms for Web services selection with end-to-end QoS constraints , 2007, TWEB.

[5]  Nima Jafari Navimipour,et al.  Cloud service composition using an inverted ant colony optimisation algorithm , 2019 .

[6]  Anne H. H. Ngu,et al.  QoS computation and policing in dynamic web service selection , 2004, WWW Alt. '04.

[7]  Hassan Mathkour,et al.  Dynamic Flying Ant Colony Optimization (DFACO) for Solving the Traveling Salesman Problem , 2019, Sensors.

[8]  Jun-Liang Chen,et al.  On the Dynamic Ant Colony Algorithm Optimization Based on Multi-pheromones , 2008, Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008).

[9]  Thomas Stützle,et al.  MAX-MIN Ant System , 2000, Future Gener. Comput. Syst..

[10]  Amir Masoud Rahmani,et al.  Privacy-aware cloud service composition based on QoS optimization in Internet of Things , 2020, Journal of Ambient Intelligence and Humanized Computing.

[11]  Liu Qinghua,et al.  A Global QoS Optimizing Web Services Selection Algorithm Based on MOACO for Dynamic Web Service Composition , 2009, 2009 International Forum on Information Technology and Applications.

[12]  Hassan Mathkour,et al.  Two-Step Artificial Bee Colony Algorithm Enhancement for QoS-Aware Web Service Selection Problem , 2019, IEEE Access.

[13]  Wang Li,et al.  A Web Service Composition Algorithm Based on Global QoS Optimizing with MOCACO , 2010, ICA3PP.

[14]  Thomas Erl,et al.  SOA Principles of Service Design (The Prentice Hall Service-Oriented Computing Series from Thomas Erl) , 2007 .

[15]  Li Ma,et al.  The Application of Ant Colony Algorithm in Web Service Selection , 2010, 2010 International Conference on Computational Intelligence and Software Engineering.

[16]  Rajkumar Buyya,et al.  Optimal Fitness Aware Cloud Service Composition using an Adaptive Genotypes Evolution based Genetic Algorithm , 2019, Future Gener. Comput. Syst..

[17]  A Senthilkumar,et al.  Swarm Intelligence from Natural to Artificial Systems: Ant Colony Optimization , 2016 .

[18]  Wei Zhang,et al.  QoS-Based Dynamic Web Service Composition with Ant Colony Optimization , 2010, 2010 IEEE 34th Annual Computer Software and Applications Conference.

[19]  Dac-Nhuong Le,et al.  A New Ant-Based Approach for Optimal Service Selection with E2E QoS Constraints , 2015, SOCO 2015.

[20]  Mohsen Kahani,et al.  Semantic web service composition based on ant colony optimization method , 2009, 2009 First International Conference on Networked Digital Technologies.

[21]  Jun Shen,et al.  QoS-Aware Peer Services Selection Using Ant Colony Optimisation , 2009, BIS.

[22]  Gang Xue,et al.  An optimization ant colony algorithm for composition of semantic Web services , 2009, 2009 Asia-Pacific Conference on Computational Intelligence and Industrial Applications (PACIIA).

[23]  Daniel A. Menascé,et al.  Composing Web Services: A QoS View , 2004, IEEE Internet Comput..

[24]  Valérie Issarny,et al.  QoS-Aware Service Composition in Dynamic Service Oriented Environments , 2009, Middleware.

[25]  Bin Zhang,et al.  A Novel Ant Colony Optimization Algorithm for Large Scale QoS-Based Service Selection Problem , 2013 .

[26]  Tudor David,et al.  Ant-inspired Framework for Automatic Web Service Composition , 2011, Scalable Comput. Pract. Exp..

[27]  Hamed Bouzary,et al.  A hybrid grey wolf optimizer algorithm with evolutionary operators for optimal QoS-aware service composition and optimal selection in cloud manufacturing , 2018, The International Journal of Advanced Manufacturing Technology.

[28]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[29]  Pinar Civicioglu,et al.  A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms , 2013, Artificial Intelligence Review.

[30]  M Dorigo,et al.  Ant colonies for the travelling salesman problem. , 1997, Bio Systems.

[31]  Ahmed Ghoneim,et al.  Enhanced Artificial Bee Colony Algorithm for QoS-aware Web Service Selection problem , 2017, Computing.

[32]  Alaa Aljanaby.  An Experimental Study of the Search Stagnation in Ants Algorithms , 2016 .

[33]  Ahmed Ghoneim,et al.  An Adapted Ant-Inspired Algorithm for Enhancing Web Service Composition , 2017, Int. J. Semantic Web Inf. Syst..

[34]  Luca Maria Gambardella,et al.  Solving symmetric and asymmetric TSPs by ant colonies , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[35]  Tudor David,et al.  Ant-Inspired Technique for Automatic Web Service Composition and Selection , 2010, 2010 12th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.

[36]  Jiapeng Xiu,et al.  The ant colony Optimization Algorithm for web services composition on Preference Ontology , 2011 .

[37]  Hao Huang,et al.  A Novel Adaptive Web Service Selection Algorithm Based on Ant Colony Optimization for Dynamic Web Service Composition , 2014, ICA3PP.

[38]  Adam Lipowski,et al.  Roulette-wheel selection via stochastic acceptance , 2011, ArXiv.

[39]  Xiao Zheng,et al.  Ant Colony System Based Algorithm for QoS-Aware Web Service Selection , 2007, GSEM.

[40]  Ansuman Banerjee,et al.  QoS-aware Automatic Web Service Composition with Multiple Objectives , 2018, ACM Trans. Web.

[41]  Mike P. Papazoglou,et al.  Web Services - Principles and Technology , 2007 .

[42]  Sunil R Dhore.  QoS Based Web Services Composition using Ant Colony Optimization: Mobile Agent Approach , 2012 .

[43]  Ma Lin,et al.  An Improved Ant Colony Optimization Algorithm for QoS-Aware Dynamic Web Service Composition , 2012, 2012 International Conference on Industrial Control and Electronics Engineering.

[44]  Li Ma,et al.  The Research of Web Service Selection Based on the Ant Colony Algorithm , 2010, 2010 International Conference on Artificial Intelligence and Computational Intelligence.

[45]  Hassan Mathkour,et al.  Enhancement of Ant Colony Optimization for QoS-Aware Web Service Selection , 2019, IEEE Access.

[46]  Tudor David,et al.  An ant-inspired approach for semantic web service clustering , 2010, 9th RoEduNet IEEE International Conference.

[47]  Qingtang Liu,et al.  A Dynamic Web Services Composition Algorithm Based on the Combination of Ant Colony Algorithm and Genetic Algorithm , 2010 .

[48]  V. Chifu,et al.  ANT-BASED METHODS FOR SEMANTIC WEB SERVICE DISCOVERY AND COMPOSITION , 2010 .

[49]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[50]  Bo Yang,et al.  An enhanced multi-objective grey wolf optimizer for service composition in cloud manufacturing , 2020, Appl. Soft Comput..