Correlation-aware manufacturing service composition model using an extended flower pollination algorithm

Zhang, Wenyu; Yang, Yushu; Yu, Dejian; Li, Yacheng

doi:10.1080/00207543.2017.1402137

Cited by 43 publications

(7 citation statements)

References 41 publications

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“…Zhang, Yang [30] proposed a service composition system based on a flower pollination algorithm. This approach is used to classify the services based on their inconsistencies with respect to the QoS parameters.…”

Section: Metaheuristic Methodsmentioning

confidence: 99%

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A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm

Sefati

Halunga

2022

Sensors

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The rapid development of Cloud Computing (CC) has led to the release of many services in the cloud environment. Service composition awareness of Quality of Service (QoS) is a significant challenge in CC. A single service in the cloud environment cannot respond to the complex requests and diverse requirements of the real world. In some cases, one service cannot fulfill the user’s needs, so it is necessary to combine different services to meet these requirements. Many available services provide an enormous QoS and selecting or composing those combined services is called an Np-hard optimization problem. One of the significant challenges in CC is integrating existing services to meet the intricate necessities of different types of users. Due to NP-hard complexity of service composition, many metaheuristic algorithms have been used so far. This article presents the Artificial Bee Colony and Genetic Algorithm (ABCGA) as a metaheuristic algorithm to achieve the desired goals. If the fitness function of the services selected by the Genetic Algorithm (GA) is suitable, a set of services is further introduced for the Artificial Bee Colony (ABC) algorithm to choose the appropriate service from, according to each user’s needs. The proposed solution is evaluated through experiments using Cloud SIM simulation, and the numerical results prove the efficiency of the proposed method with respect to reliability, availability, and cost.

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Section: Metaheuristic Methodsmentioning

confidence: 99%

“…Response times were divided (0.5, 2, 3 and etc.) and availability was divided in values (99.5, 99.9 and 99.999) and, also, for costs there were values (5,20,30,40). After sorting based on QWS, we obtained a dataset with 50 services for the six chosen criteria.…”

Section: The Simulation Data Parametersmentioning

confidence: 99%

A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm

Sefati

Halunga

2022

Sensors

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“…Existing research is often based on the idea of QoS guarantee, from the perspective of reliability, security, and other nonfunctional attributes of service computing research, especially through the combination of atomic services to maximize the needs of users [10,11]. For Web service composition in the study, the researcher proposed the service selection method based on mathematical programming; service selection method based on genetic algorithm (GA); service selection method based on particle swarm optimization (PSO) algorithm, based on swarm intelligence computing; service selection method based on global QoS constraint decomposition; service selection method based on artificial intelligence theory; and many other kinds of service composition methods [12][13][14].…”

Section: Related Workmentioning

confidence: 99%

“…, i − 1)], which is less than i in the time coordinate. Of course, in practical application, to reduce the storage capacity, the actual value stored in the data center is the actual measured value and the estimated value at the last execution of the Web service which is calculated by (14), rather than recording all measurements of the historical execution time of the atomic service.…”

Section: Qos Prediction Experiments For Random Changesmentioning

confidence: 99%

Trusted QoS Assurance Approach for Composite Service

Cai

Cui

et al. 2021

Security and Communication Networks

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The characteristics of service computing environment, such as service loose coupling, resource heterogeneity, and protocol independence, propose higher demand for the trustworthiness of service computing systems. Trust provisioning for composite services has become a hot research spot worldwide. In this paper, quality of service (QoS) planning techniques are introduced into service composition-oriented QoS provisioning architecture. By QoS planning, the overall QoS requirement of the composite service is decomposed into separate QoS requirement for every constituent atom service, the QoS level of which can subsequently be satisfied through well-designed service entity selection policies. For any single service entity, its QoS level is variable when the deployment environment or the load of service node changes. To mitigate the uncertainty, we put forward QoS preprocessing algorithms to estimate the future QoS levels of service entities with their history execution data. Then, based on the modeling of composite service and QoS planning, we design three algorithms, which include the time preference algorithm, cost/availability (C/A) preference algorithm, and Euclidean distance preference algorithm, to select suitable atomic services meeting the user’s requirements. Finally, by combining genetic algorithm and local-search algorithm, we propose memetic algorithm to meet the QoS requirements of composite service. The effectiveness of the proposed methods by which the QoS requirements can be satisfied up to 90% is verified through experiments.

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“…The cross-pollination stage uses the Lévy flight search strategy (Gao et al, 2018). FPA has the advantages of few parameters, simple implementation, and novel optimization structure, so it has attract widespread concern in SCOS fields (Zhang et al, 2018a, 2018b, 2019b). Based on the advantages of FPA, for better optimization, some scholars have extended it to methods such as modified global flower pollination flower algorithm-MGFPA (Shambour et al, 2019) and differential evolution flower pollination algorithm-MCFPA (Liu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A discrete manufacturing SCOS framework based on functional interval parameters and fuzzy QoS attributes using moving window FPA

Gao

Yan

Guo

2021

Concurrent Engineering

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Manufacturing service composition and optimal selection (SCOS) is a key technology that improves resource utilization and reduces the cost in discrete manufacturing. However, the lack of evaluation of the service composition function and the unconformity of the actual composition vague characteristics, resulting in the incomplete evaluation of the service composition. Additionally, various optimization and selection algorithms have defects of premature convergence and low efficiency. At the same time, the fitness value distribution of the service composition has a non-linear characteristic. In this article, a framework called discrete manufacturing SCOS (DMSCOS) is proposed to overcome these issues. DMSCOS uses the functional interval parameter and fuzzy QoS attribute aware evaluation model (FIPFQA) to achieve composition evaluation and introduces a moving window flower pollination algorithm (MWFPA) to achieve optimization and selection for the non-linear characteristic population. Experiments show that DMSCOS has good performance for optimization and selection. The FIPFQA has a good effect on service composition evaluation. Furthermore, compared with two other extended algorithms, the proposed MWFPA performs better when addressing the optimal and selection problem.

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Correlation-aware manufacturing service composition model using an extended flower pollination algorithm

Cited by 43 publications

References 41 publications

A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm

A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm

Trusted QoS Assurance Approach for Composite Service

A discrete manufacturing SCOS framework based on functional interval parameters and fuzzy QoS attributes using moving window FPA

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