A hybrid teaching-learning-based optimization algorithm for QoS-aware manufacturing cloud service composition

Jiang, Hong; Cheng, Jiang; Lv, Shengping; He, Haiping; Liao, Xinting

doi:10.1007/s00607-022-01083-4

Cited by 8 publications

(4 citation statements)

References 36 publications

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“…However, the essence of these approaches is multi-objective optimization. Therefore, we select three representative meta-heuristic algorithms used for QoS-aware manufacturing service composition as baselines, i.e., GA (Genetic Algorithm) [3], PSO (Particle Swarm Optimization) [7], and TLBO (Teaching-Learning-Based Optimization) [31]. Since the Skyline technique is used in our approach, we also compare meta-heuristic algorithms with Skyline services in the preliminary service selection of atomic tasks.…”

Section: Methodsmentioning

confidence: 99%

“…In the case study, SPEA-II and NSGA-II are used to generate manufacturing service compositions with both effect and high collaboration frequency indicators. Jin et al [31] proposed a novel hybrid teaching-learning-based optimization algorithm to solve QoS-aware manufacturing cloud service composition problems, in which the advantages of uniform mutation, adaptive flower pollination algorithm, and the teaching-learning-based optimization algorithm are integrated. Besides, there are still some other works on manufacturing service composition, while their core idea still focuses on meta-heuristic algorithms by taking different factors into account.…”

Section: Related Workmentioning

confidence: 99%

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Scalable and Optimal QoS-Aware Manufacturing Service Composition via Business Process Decomposition

et al. 2023

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With the adoption of service-oriented manufacturing modes, more and more manufacturing services are released over manufacturing service platforms. As it is known, the problem of the QoS (Quality of Service)-aware manufacturing service composition is NP-hard. Thus, the optimization remains a challenging research issue, especially in the situation of large-scale manufacturing service data which arouse a scalability problem as well. To improve both the optimization performance and scalability of the QoS-aware manufacturing service composition, this paper proposes a scalable and optimal QoS-aware manufacturing service composition approach via business process decomposition. Specifically, the service composition process is decomposed by using a refined process structure tree (RPST). Moreover, an optimized service composition is achieved layer by layer based on the refined process structure tree in a bottom-up manner. For the atomic tasks or the compound tasks in the same layer of RPST, the corresponding QoS-aware service selection is optimized by calculating Skyline services, which can be carried out in parallel if necessary. When the optimization arrives at the root node, the complete service composition plans are derived. In our approach, the optimal manufacturing service candidates are picked out stage by stage. In this way, both the optimality and scalability of the whole approach can be guaranteed. Extensive experiments are conducted to verify the optimality and scalability of our approach.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Scalable and Optimal QoS-Aware Manufacturing Service Composition via Business Process Decomposition

et al. 2023

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“…Since QoS-based service composition methods are NP-hard problems, a common method to solve this problem is to transform the multi-objective optimization problem of service composition preference into a single-objective optimization problem, and then solve it using mature heuristic algorithms, such as genetic algorithm [24,25], artificial bee colony algorithm [26], particle swarm optimization algorithm [27], ant colony optimization algorithm [28], chaos algorithm [ 29], etc., and also scholars use the advantages of each algorithm to design hybrid algorithms to solve the service composition problem, such as Wang et al [30] proposed a novel hybrid algorithm called Bee-Colony Simplex method hybrid Algorithm (ABCSA), which uses the simplex method and chaotic global optimal guidance strategy. Jin et al [31] proposed a new hybrid teaching-based optimization algorithm to solve the QoS-MCSC problem, which combines the advantages of uniform mutation, adaptive pollination algorithm and teaching-based optimization algorithm. Gavvala et al [32] proposed a new Eagle Strategy with Whale Optimization Algorithm (ESWOA) to ensure a proper balance between exploration and development.…”

Section: Service Combination Based On Heuristic Algorithmmentioning

confidence: 99%

A matching-synergy degree-based optimization selection method for cloud manufacturing service composition

Chao¹,

Li²,

2023

Preprint

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In a cloud manufacturing scenario, the matching degree between manufacturing tasks and cloud services, as well as the synergy degree between multiple selected cloud services are key metrics to measure the benefits of cloud manufacturing service application. However, the latter is often overlooked in the cloud service selection process. In this paper, a matching-synergy analysis-based optimization method of cloud manufacturing service composition is proposed. Firstly, an evaluation system of cloud service composition quality is established, including service matching degree (SM), service composition synergy (CS), and other metrics, such as service time (T), service cost (C) and reliability (R). Secondly, considering the interests of both service requestors and service resource providers, a two-constraint combination preference model is constructed, and solved by using the improved ant colony algorithm (IACO). Finally, the feasibility and effectiveness of the proposed method are verified with the example of an automobile bumper cloud service.

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“…The process of developing different Web services is an optimization problem regarding the global QoS. However, this optimization problem is NP-hard [5][6][7]. The QoSaware Web service composition problem becomes more difficult when many Web services possess similar QoS.…”

Section: Introductionmentioning

confidence: 99%

QoS-Centric Diversified Web Service Recommendation Based on Personalized Determinantal Point Process

Kang

Liang

et al. 2023

Electronics

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With the popularity and widespread adoption of the SOA (Service-Oriented Architecture), the number of Web services has increased exponentially. Users tend to use online services for their daily business and software development needs. With the large number of Web service candidates, recommending desirable Web services that meet users’ personalized QoS (Quality of Service) requirements becomes a challenging research issue, as the QoS preference is usually difficult to satisfy for users, i.e., the QoS preference is uncertain. To solve this problem, some recent works have aimed to recommend QoS-diversified services to enhance the probability of fulfilling the user’s latent QoS preferences. However, the existing QoS-diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the personalized diversity preference requirements are not considered. To this end, this paper proposes to mine a user’s diversity preference from the their service invocation history and provides a Web service recommendation algorithm, named PDPP (Personalized Determinantal Point Process), through which a personalized service recommendation list with preferred diversity is generated for the user. Comprehensive experimental results show that the proposed approach can provide personalized and diversified Web services while ensuring the overall accuracy of the recommendation results.

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A hybrid teaching-learning-based optimization algorithm for QoS-aware manufacturing cloud service composition

Cited by 8 publications

References 36 publications

Scalable and Optimal QoS-Aware Manufacturing Service Composition via Business Process Decomposition

Scalable and Optimal QoS-Aware Manufacturing Service Composition via Business Process Decomposition

A matching-synergy degree-based optimization selection method for cloud manufacturing service composition

QoS-Centric Diversified Web Service Recommendation Based on Personalized Determinantal Point Process

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