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During the process of cloud manufacturing, various uncertainties in the real world could have a significant impact on the smooth execution of task, and could render the planned composite manufacturing service (CMS) inefficient or even ineffective. Therefore, this paper proposes an optimal selection method to enhance the robustness of CMS during the planning stage. Firstly, the structure of robust CMS is proposed by arranging the preferred and alternative services for each subtask, and a robust service composition and optimal selection (rSCOS) model of cloud manufacturing is constructed by defining the expected Quality of Service. Then, the gABC-GWO (guiding artificial bee colony – grey wolf optimisation) algorithm is proposed to solve the rSCOS model efficiently, in which three improvement strategies for ABC algorithm are designed according to the characteristics of GWO. Finally, two experiments are implemented and the results show that QoS of the preferred scheme of robust CMS is approximately 1.29% lower than that of CMS on average, while its robustness is improved by 1.81% and 13.14% depending on the two robustness indexes. Compared with other commonly-used intelligence optimisation algorithms, gABC-GWO algorithm possesses better search performance without significantly increasing time consumption, which makes it more suitable for solving rSOCS problems. [ABSTRACT FROM AUTHOR]

At present, with the emergence and development of cloud manufacturing (CMfg), the scale of services in CMfg platforms increases rapidly which provide the same or familiar functionality but different performance. Large-scale cloud service composition and optimization (CSCO) problems is one of the key issues for the implementation of CMfg. To deal with this NP-hard problem, a novel hybrid algorithm called Bee-Colony Simplex method hybrid Algorithm (ABCSA) for CSCO problems is proposed in this paper, which employs both the Simplex method and chaotic and global best guided strategy. The random-evolve Simplex method is proposed to maintain the algorithm work efficiently to keep the population diversity and avoid premature convergence. The global best guided and chaos searching strategy is proposed to avoid local optimization. To evaluate the effectiveness and efficiency, simulation and analysis of the experiments are carried out, and the results clearly prove the superior performance of ABCSA over existing intelligent optimization algorithms in the CSCO problems. [ABSTRACT FROM AUTHOR]

In the cloud manufacturing service model, a good trust relationship among trading entities including manufacturing service providers and consumers is the basis for service transactions. To characterize the cooperative trust effectively, the evaluation index system was built to quantize the service satisfaction by monitoring the data of historical service evaluation, and then a service satisfaction-based trust evaluation model was proposed. The direct satisfaction, the friend recommendation satisfaction and the platform satisfaction were integrated into the comprehensive trust. To improve the reliability of the proposed model, the time decay function was utilized to characterize the change of service satisfaction over time. Meanwhile, the comprehensive trust was corrected by using the service satisfaction volatility, which was taken as a mechanism of reward and punishment in the long-term transaction process. The experimental results verified that the model reflected the subjective preference of consumers and showed good accuracy and efficiency, especially in the case of big transaction data. [ABSTRACT FROM AUTHOR]

As an important part of cloud manufacturing, service composition and optimal selection (SCOS) has attracted many scholars' attentions. Although there are a large number of researches devoted to this field, plenty of challenges still remain, such as the difficulties of coping with real-life uncertainties and improving efficiency in making decisions. To this end, a bi-objective service composition and optimal selection (BoSCOS) problem which takes both QoS and robustness into account is firstly studied in this paper, its mathematical model is established after the two criteria are constructed. Then, a strengthened multi-objective gray wolf optimizer (SMOGWO) is developed to solve the above model with high efficiency, in which three improvement strategies are introduced to the original MOGWO: (1) the opposition based learning (OBL) strategy is adopted to improve the quality of initial population; (2) a leader update strategy is designed to explore better search leaders and balance the exploitation and exploration abilities; (3) an improved archiving strategy is developed to retain all possible dominant individuals, so as to improve the population diversity based on the Pareto dominance. Finally, the effectiveness of the proposed algorithm is verified on 8 BoSCOS problems with different scales and 17 benchmark functions, results show that the improvement strategies are effective and SMOGWO is superior to its competitors in terms of convergence and population diversity. • A BoSCOS problem considering both QoS and robustness is firstly proposed. • The robustness of a CMS is formulated in terms of response time caused by exceptions. • A leader update strategy is designed to explore better search leaders for SMOGWO. • An improved archive mechanism is developed to improve the population diversity. • Experimental results show that SMOGWO is obvious superior to common-used algorithms. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced multi-objective grey wolf optimizer (EMOGWO) for multi-objective service composition and optimal selection (MO-SCOS) problem in cloud manufacturing, wherein both the quality of service and the energy consumption are considered from the perspectives of sustainable manufacturing. Given that there are still deficiencies in local optimum and diversity for the original multi-objective grey wolf optimizer (MOGWO). in response, the backward learning strategy is employed to heighten the exploration of initial population, so as to increase the search efficiency. Then, in order to improve the diversity, a nonlinear adjustment strategy for control parameter is proposed to enhance the global exploration of the algorithm. Besides, an enhanced search strategy, which strengthens the exploration of leaders, is designed to avoid the local optimum. Finally, the comparative study with typical multi-objective algorithms for MO-SCOS problems and benchmark problems are carried out to verify the proposed approach. simulation results suggest that the improvements for MOGWO are effective, and the proposed EMOGWO obtains better performance over other compared algorithms. • An enhanced multi-objective grey wolf optimizer (EMOGWO) is proposed. • Backward learning is employed to increase the diversity of initial population. • A novel control parameter is designed to enhance the exploration of grey wolves. • An enhanced search strategy is proposed to assist to escape from the local optimum. [ABSTRACT FROM AUTHOR]