Multi-objective quantum inspired Cuckoo search algorithm and multi-objective bat inspired algorithm for the web service composition problem

One of the most interesting challenges introduced by web services is the automatic web service composition design. The goal is to obtain an optimal web service composition by combining existing ones. In this paper two optimisation methods are proposed to design the best composition, a multi-objective quantum inspired Cuckoo search algorithm and a multi-objective bat inspired algorithm. The particularity of the approach is that the composition solution is gradually built using one of the two algorithms starting from the user request. Another particularity is that two optimisation criteria are considered, the quality of service and the semantic distance. The multi-criteria selection is handled by considering the Pareto front which ensures that no criteria can be improved without degrading another one. A prototype has been realised and applied to a text translation case study. The obtained results from the experimentations are encouraging and proves the feasibility and effectiveness of the approach.

[1]  Xiaomeng Su,et al.  A Survey of Automated Web Service Composition Methods , 2004, SWSWPC.

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

[3]  Abdesslem Layeb,et al.  A Novel Quantum Inspired Cuckoo Search Algorithm for Bin Packing Problem , 2012 .

[4]  El-Ghazali Talbi,et al.  A multiobjective genetic algorithm for radio network optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[5]  Dieter Fensel,et al.  Semantic Web Services , 2011, Handbook on Ontologies.

[6]  Jianjun Li,et al.  Research on Intelligence Optimization of Web Service Composition for QoS , 2012, ICICA.

[7]  Junli Wang,et al.  Optimal Web Service Selection based on Multi-Objective Genetic Algorithm , 2008, 2008 International Symposium on Computational Intelligence and Design.

[8]  Xin-She Yang,et al.  Engineering optimisation by cuckoo search , 2010 .

[9]  Almoataz Y. Abdelaziz,et al.  Reactive power compensation in distribution networks using cuckoo search algorithm , 2014, Int. J. Bio Inspired Comput..

[10]  Abdesslem Layeb,et al.  A novel quantum inspired cuckoo search for knapsack problems , 2011, Int. J. Bio Inspired Comput..

[11]  Rafael S. Parpinelli,et al.  New inspirations in swarm intelligence: a survey , 2011, Int. J. Bio Inspired Comput..

[12]  Ioan Salomie,et al.  Immune-Inspired Method for Selecting the Optimal Solution in Web Service Composition , 2009, RED.

[13]  Hisao Ishibuchi,et al.  Multi-objective genetic local search algorithm , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[14]  Tran Cao Son,et al.  Adapting Golog for Composition of Semantic Web Services , 2002, KR.

[15]  Naser Nematbakhsh,et al.  A Multi-Objective Particle Swarm Optimization for Web Service Composition , 2010, NDT.

[16]  Chris Preist.  A Conceptual Architecture for Semantic Web Services , 2004, International Semantic Web Conference.

[17]  Freddy Lécué,et al.  Optimizing QoS-Aware Semantic Web Service Composition , 2009, SEMWEB.

[18]  J. Dréo,et al.  Métaheuristiques pour l'optimisation difficile , 2003 .

[19]  Xin-She Yang,et al.  Bat algorithm: literature review and applications , 2013, Int. J. Bio Inspired Comput..

[20]  Kurt Geihs,et al.  Making a Fast Semantic Service Composition System Faster , 2007, The 9th IEEE International Conference on E-Commerce Technology and The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services (CEC-EEE 2007).

[21]  D. Haussler,et al.  MUTUAL INFORMATION, METRIC ENTROPY AND CUMULATIVE RELATIVE ENTROPY RISK , 1997 .

[22]  Stephan Reiff-Marganiec,et al.  Towards Heuristic Web Services Composition Using Immune Algorithm , 2008, 2008 IEEE International Conference on Web Services.

[23]  Lijuan Wang,et al.  A survey on bio-inspired algorithms for web service composition , 2012, Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[24]  OuederniMeriem,et al.  Multi-objective quantum inspired Cuckoo search algorithm and multi-objective bat inspired algorithm for the web service composition problem , 2016 .

[25]  Liang Chen,et al.  Web Service Composition Optimization Based on Improved Artificial Bee Colony Algorithm , 2013, J. Networks.

[26]  Ioan Salomie,et al.  Hybrid immune-inspired method for selecting the optimal or a near-optimal service composition , 2011, 2011 Federated Conference on Computer Science and Information Systems (FedCSIS).

[27]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[28]  El-Ghazali Talbi,et al.  Design of multi-objective evolutionary algorithms: application to the flow-shop scheduling problem , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[29]  Razamin Ramli,et al.  Recent advancements of nurse schedulingmodels and a potential path , 2010 .

[30]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[31]  Djamil Aïssani,et al.  Semantic annotations for web services discovery and composition , 2009, Comput. Stand. Interfaces.

[32]  Thomas Risse,et al.  Combining global optimization with local selection for efficient QoS-aware service composition , 2009, WWW '09.

[33]  Jin-Kao Hao,et al.  Selecting Web Services for Optimal Composition , 2005, SDWP@ICWS.

[34]  Abdesslem Layeb,et al.  A hybrid quantum inspired harmony search algorithm for 0-1 optimization problems , 2013, J. Comput. Appl. Math..

[35]  Ioan Salomie,et al.  Exploring the Selection of the Optimal Web Service Composition through Ant Colony Optimization , 2014, Comput. Informatics.

[36]  Xin-She Yang,et al.  BBA: A Binary Bat Algorithm for Feature Selection , 2012, 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images.

[37]  Andreas Abecker,et al.  Semantic Web Services: Concepts, Technologies, and Applications , 2010 .

[38]  Gustavo Alonso,et al.  Web Services: Concepts, Architectures and Applications , 2009 .

[39]  Allaoua Chaoui,et al.  Optimizing QoS-Based Web Services Composition by Using Quantum Inspired Cuckoo Search Algorithm , 2014, MobiWIS.

[40]  Salim Chikhi,et al.  Quantum inspired cuckoo search algorithm for graph colouring problem , 2015, Int. J. Bio Inspired Comput..

[41]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[42]  Freddy Lécué,et al.  Seeking Quality of Web Service Composition in a Semantic Dimension , 2011, IEEE Transactions on Knowledge and Data Engineering.

[43]  Manoj Duhan,et al.  Bat Algorithm: A Survey of the State-of-the-Art , 2015, Appl. Artif. Intell..

[44]  Lifeng Ai,et al.  A hybrid genetic algorithm for the optimal constrained web service selection problem in web service composition , 2010, IEEE Congress on Evolutionary Computation.