A novel quality-of-service-aware web services composition using biogeography-based optimization algorithm

With the development of technology and computer systems, web services are used to develop business processes. Since a web service only performs a simple operation, web services composition has become important to respond to these business processes. In recent times, the number of existing web services has grown increasingly; therefore, similar services are presented increasingly. These similar web services are discriminated based on the various quality of service (QoS) parameters. These quality parameters include cost, execution time, availability, and reliability. In order to have the best QoS, each user should select a subset of services that presents best quality parameters. On the other hand, due to huge number of services, selecting web services for composition is an NP-hard optimization problem. This paper presents an efficient method for solving this problem using biogeography-based optimization (BBO). BBO is a very simple algorithm with few control parameters and effective exploit. The proposed method offers promising solutions to this problem. Evaluation and simulation results indicate efficiency and feasibility of the proposed algorithm.

[1]  Jianquan Cheng,et al.  Evolution of the Cultural Trade Network in “the Belt and Road” Region: Implication for Global Cultural Sustainability , 2019, Sustainability.

[2]  Ching-Hsin Wang,et al.  Biogeography-based optimization based on population competition strategy for solving the substation location problem , 2018, Expert Syst. Appl..

[3]  Thomas Risse,et al.  Selecting skyline services for QoS-based web service composition , 2010, WWW '10.

[4]  Yudong Zhang,et al.  Automated classification of brain images using wavelet-energy and biogeography-based optimization , 2016, Multimedia Tools and Applications.

[5]  Jouni Lampinen,et al.  GDE3: the third evolution step of generalized differential evolution , 2005, 2005 IEEE Congress on Evolutionary Computation.

[6]  Sergio Segura,et al.  Evolutionary composition of QoS-aware web services: A many-objective perspective , 2017, Expert Syst. Appl..

[7]  Seyed Mostafa Bozorgi,et al.  A new clustering protocol for energy harvesting-wireless sensor networks , 2017, Comput. Electr. Eng..

[8]  A. Kai Qin,et al.  Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[9]  K. Deb.  An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[10]  Yudong Zhang,et al.  Single slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization , 2018, Multimedia Tools and Applications.

[12]  Dan Simon,et al.  Blended biogeography-based optimization for constrained optimization , 2011, Eng. Appl. Artif. Intell..

[13]  Mohamed Elhoseny,et al.  Extended Genetic Algorithm for solving open-shop scheduling problem , 2019, Soft Comput..

[14]  Maria Luisa Villani,et al.  An approach for QoS-aware service composition based on genetic algorithms , 2005, GECCO '05.

[15]  Wenyin Gong,et al.  DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization , 2010, Soft Comput..

[16]  Yudong Zhang,et al.  Pathological Brain Detection via Wavelet Packet Tsallis Entropy and Real-Coded Biogeography-based Optimization , 2017, Fundam. Informaticae.

[17]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[18]  Vali Derhami,et al.  QoS-Based web service composition based on genetic algorithm , 2013 .

[19]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[20]  M. Shamim Hossain,et al.  Enforcing Position-Based Confidentiality With Machine Learning Paradigm Through Mobile Edge Computing in Real-Time Industrial Informatics , 2019, IEEE Transactions on Industrial Informatics.

[21]  Mihai Alexandru Suciu,et al.  Comparative analysis of multi-objective evolutionary algorithms for QoS-aware web service composition , 2016, Appl. Soft Comput..

[22]  Arun Kumar Sangaiah,et al.  A Hybrid Unequal Clustering Based on Density with Energy Conservation in Wireless Nodes , 2019, Sustainability.

[23]  Yudong Zhang,et al.  Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization , 2016, Simul..

[24]  Russell C. Eberhart,et al.  Guest Editorial Special Issue on Particle Swarm Optimization , 2004, IEEE Trans. Evol. Comput..

[25]  Xinchao Zhao,et al.  An improved discrete immune optimization algorithm based on PSO for QoS-driven web service composition , 2012, Appl. Soft Comput..

[26]  Lei Wang,et al.  Two-stage approach for reliable dynamic Web service composition , 2016, Knowl. Based Syst..

[27]  Arun Kumar Sangaiah,et al.  A New Meta-Heuristic Algorithm for Solving the Flexible Dynamic Job-Shop Problem with Parallel Machines , 2019, Symmetry.

[28]  Yudong Zhang,et al.  Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization , 2015, Entropy.

[29]  Shahaboddin Shamshirband,et al.  TETS: A Genetic-Based Scheduler in Cloud Computing to Decrease Energy and Makespan , 2016, HIS.

[30]  Jamal Bentahar,et al.  An Efficient QoS-aware Web Services Selection Using Social Spider Algorithm , 2016, FNC/MobiSPC.

[31]  Colin J. Fidge,et al.  Partitioning composite web services for decentralized execution using a genetic algorithm , 2011, Future Gener. Comput. Syst..

[32]  Gero Mühl,et al.  QoS aggregation for Web service composition using workflow patterns , 2004 .

[33]  Quan Z. Sheng,et al.  S-ABC: A paradigm of service domain-oriented artificial bee colony algorithms for service selection and composition , 2017, Future Gener. Comput. Syst..

[34]  Junhao Wen,et al.  Fundus Image Classification Using VGG-19 Architecture with PCA and SVD , 2018, Symmetry.

[35]  Valentina Emilia Balas,et al.  OVRP_GELS: solving open vehicle routing problem using the gravitational emulation local search algorithm , 2016, Neural Computing and Applications.

[36]  Dimitris Sacharidis,et al.  Serving the Sky: Discovering and Selecting Semantic Web Services through Dynamic Skyline Queries , 2008, 2008 IEEE International Conference on Semantic Computing.

[37]  Junichi Suzuki,et al.  Multiobjective Optimization of SLA-Aware Service Composition , 2008, 2008 IEEE Congress on Services - Part I.

[38]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[39]  Xifan Yao,et al.  Multi-population parallel self-adaptive differential artificial bee colony algorithm with application in large-scale service composition for cloud manufacturing , 2017, Appl. Soft Comput..

[40]  A. Abraham,et al.  A new efficient approach for solving the capacitated Vehicle Routing Problem using the Gravitational Emulation Local Search Algorithm , 2017 .

[41]  Felix Lossin,et al.  Customer engagement for utilities: information systems to curb residential energy consumption , 2016 .

[42]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[43]  Kamran Zamanifar,et al.  QoS decomposition for service composition using genetic algorithm , 2013, Appl. Soft Comput..

[44]  David W. Coit,et al.  MOMS-GA: A Multi-Objective Multi-State Genetic Algorithm for System Reliability Optimization Design Problems , 2008, IEEE Transactions on Reliability.

[45]  Arun Kumar Sangaiah,et al.  Survey on clustering in heterogeneous and homogeneous wireless sensor networks , 2017, The Journal of Supercomputing.

[46]  Xiao Xue,et al.  Social learning optimization (SLO) algorithm paradigm and its application in QoS-aware cloud service composition , 2016, Inf. Sci..

[47]  Anne H. H. Ngu,et al.  QoS-aware middleware for Web services composition , 2004, IEEE Transactions on Software Engineering.

[48]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

[49]  Shahaboddin Shamshirband,et al.  RETRACTED ARTICLE: OSGA: genetic-based open-shop scheduling with consideration of machine maintenance in small and medium enterprises , 2015 .

[50]  Victor O. K. Li,et al.  A social spider algorithm for global optimization , 2015, Appl. Soft Comput..