A modified discrete invasive weed algorithm for optimal service composition in cloud manufacturing systems

Abstract Cloud manufacturing (CMfg), as a new service-oriented manufacturing paradigm, is aiming towards sharing and collaborating among distributed manufacturing resources and capabilities. As a result, selecting and combining these services into a composite service to meet the user’s requirements while keeping up the optimal service performances is of paramount importance. In this paper, first of all, QoS-aware service composition and optimal selection (SCOS) problem is formulated as an optimization problem and then a modified discrete invasive weed algorithm is proposed and applied as a new approach for solving the NP-hard SCOS problem in CMfg context. The algorithm which mimics the process of weed colonization and distribution, is modified by putting into effect a novel mechanism for mapping normally-distributed dispersal values into the mutation probability of corresponding dispersal direction. The experimental results prove the good performance and robustness of the approach.

[1]  Caro Lucas,et al.  A novel numerical optimization algorithm inspired from weed colonization , 2006, Ecol. Informatics.

[2]  Liang Guo,et al.  Study on machining service modes and resource selection strategies in cloud manufacturing , 2015 .

[3]  Ajit Kumar Barisal,et al.  Large scale economic dispatch of power systems using oppositional invasive weed optimization , 2015, Appl. Soft Comput..

[4]  Yixiong Feng,et al.  A fuzzy QoS-aware resource service selection considering design preference in cloud manufacturing system , 2016 .

[5]  Can Saygin,et al.  A simulation-based platform for assessing the impact of cyber-threats on smart manufacturing systems , 2018 .

[6]  Quan-Ke Pan,et al.  An effective discrete invasive weed optimization algorithm for lot-streaming flowshop scheduling problems , 2018, J. Intell. Manuf..

[7]  Xun Xu,et al.  From cloud computing to cloud manufacturing , 2012 .

[8]  Chen Yang,et al.  IoT-enabled dynamic service selection across multiple manufacturing clouds , 2016 .

[9]  Xifan Yao,et al.  A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition , 2017, Int. J. Prod. Res..

[10]  Maheswarapu Sydulu,et al.  Multi-Objective Invasive Weed Optimization – An application to optimal network reconfiguration in radial distribution systems , 2015 .

[11]  Feng Xiang,et al.  The case-library method for service composition and optimal selection of big manufacturing data in cloud manufacturing system , 2016 .

[12]  Feng Li,et al.  A clustering network-based approach to service composition in cloud manufacturing , 2017, Int. J. Comput. Integr. Manuf..

[13]  Xifan Yao,et al.  Correlation-aware QoS modeling and manufacturing cloud service composition , 2017, J. Intell. Manuf..

[14]  T. Jayabarathi,et al.  Optimal placement and sizing of multiple distributed generating units in distribution networks by invasive weed optimization algorithm , 2016 .

[15]  Pavlos I. Lazaridis,et al.  Synthesis of a Near-Optimal High-Gain Antenna Array With Main Lobe Tilting and Null Filling Using Taguchi Initialized Invasive Weed Optimization , 2014, IEEE Transactions on Broadcasting.

[16]  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..

[17]  D. P. Kothari,et al.  Unit commitment problem solution using invasive weed optimization algorithm , 2014 .

[18]  Wenjun Xu,et al.  An improved discrete bees algorithm for correlation-aware service aggregation optimization in cloud manufacturing , 2016 .

[19]  Hong Liu,et al.  A Cloud Manufacturing Resource Allocation Model Based on Ant Colony Optimization Algorithm , 2015 .

[20]  Liangli Ma,et al.  An Efficient Discrete Invasive Weed Optimization Algorithm for Web Services Selection , 2014, J. Softw..

[21]  Jinzhao Wu,et al.  A discrete invasive weed optimization algorithm for solving traveling salesman problem , 2015, Neurocomputing.

[22]  Yefa Hu,et al.  QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system , 2014, Central Eur. J. Oper. Res..

[23]  Meng Yen Shih,et al.  Improvement of non-standardized directional overcurrent relay coordination by invasive weed optimization , 2018 .

[24]  Shuai Zhang,et al.  A New Manufacturing Service Selection and Composition Method Using Improved Flower Pollination Algorithm , 2016 .

[25]  Jun-Qing Li,et al.  An effective invasive weed optimization algorithm for scheduling semiconductor final testing problem , 2018, Swarm Evol. Comput..

[26]  Yongquan Zhou,et al.  Invasive weed optimization algorithm for optimization no-idle flow shop scheduling problem , 2014, Neurocomputing.

[27]  Dechang Pi,et al.  Self-adaptive discrete invasive weed optimization for the blocking flow-shop scheduling problem to minimize total tardiness , 2017, Comput. Ind. Eng..

[28]  Zhi xin Zheng,et al.  Optimal chiller loading by improved invasive weed optimization algorithm for reducing energy consumption , 2018 .

[29]  S. K. Mishra,et al.  An invasive weed optimization approach for job shop scheduling problems , 2017 .

[30]  Mojtaba Ghasemi,et al.  Solving non-linear, non-smooth and non-convex optimal power flow problems using chaotic invasive weed optimization algorithms based on chaos , 2014 .

[31]  Dechen Zhan,et al.  Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved Artificial Bee Colony optimisation algorithm , 2015 .

[32]  Xifan Yao,et al.  Multi-objective Optimization of Cloud Manufacturing Service Composition with Cloud-Entropy Enhanced Genetic Algorithm , 2016 .