Cuckoo-inspired hybrid algorithm for selecting the optimal Web service composition

Pop, Cristina Bianca; Chifu, Viorica Rozina; Salomie, Ioan; Vlad, Monica

doi:10.1109/iccp.2011.6047841

Cited by 12 publications

(4 citation statements)

References 8 publications

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“…Other Hybrid Methods also appeared frequently in the existing literature by given dominance to the newest metaheuristics. Pop et al [174] combined cuckoo search with reinforcement learning and Tabu search. In this arrangement, enforcement learning keeps track of service replacement while tabu search, a trajectory metaheuristic set to accelerate the slow convergence.…”

Section: Classification Of Hybrid Metaheuristicmentioning

confidence: 99%

“…In recent years, Machine Learning (ML) has gradually become a viable hybrid strategy to incorporate into metaheuristics. The machine learning techniques including Qlearning [137], deep learning [156], reinforcement learning [174] has been employed to improve the search mechanism on different grounds. While machine learning incorporated methods generating a high-quality population, they are not highly competitive from the time of execution perspective attributed to the sophistication of ML enabled solutions.…”

Section: S-5mentioning

confidence: 99%

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Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

et al. 2022

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With the advent of Service-Oriented Architecture (SOA), services can be registered, invoked, and combined by their identical Quality of Services (QoS) attributes to create a new value-added application that fulfils user requirements. Efficient QoS-aware service composition has been a challenging task in cloud computing. This challenge becomes more formidable in emerging resource-constrained computing paradigms such as the Internet of Things and Fog. Service composition has regarded as a multi-objective combinatorial optimization problem that falls in the category of NP-hard. Historically, the proliferation of services added to problem complexity and navigated solutions from exact (none-heuristics) approaches to near-optimal heuristics and metaheuristics. Although metaheuristics have fulfilled some expectations, the quest for finding a high-quality, near-optimal solution has led researchers to devise hybrid methods. As a result, research on service composition shifts towards the hybridization of metaheuristics. Hybrid metaheuristics have been promising efforts to transcend the boundaries of metaheuristics by leveraging the strength of complementary methods to overcome base algorithm shortcomings. Despite the significance and frontier position of hybrid metaheuristics, to the best of our knowledge, there is no systematic research and survey in this field with a particular focus on strategies to hybridize traditional metaheuristics. This study's core contribution is to infer a framework for hybridization strategies by conducting a mapping study that analyses 71 papers between 2008 and 2020. Moreover, it provides a panoramic view of hybrid methods and their experiment setting in respect to the problem domain as the main outcome of this mapping study. Finally, research trends, directions and challenges are discussed to benefit future endeavours.

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Section: Classification Of Hybrid Metaheuristicmentioning

confidence: 99%

Section: S-5mentioning

confidence: 99%

Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

et al. 2022

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“…Their experimental results indicated that the proposed solution could result in the reduction of service composition time and the enhancement of the optimality of compound services. Pop et al proposed a hybrid algorithm based on the cuckoo search for finding the best WSC. Their solution hybrids the cuckoo search and the tabu search with reinforcement learning to improve the selection process in terms of execution time and explored search space.…”

Section: Related Workmentioning

confidence: 99%

A moth‐flame optimization algorithm for web service composition in cloud computing: Simulation and verification

et al. 2018

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In recent years, users are becoming increasingly accustomed to using the Internet to gain software resources in the form of web services provided by information technology organizations. Cloud computing is a service delivery paradigm that shares services and resources to access the web services to the end users over the Internet. In the cloud environment, based on the user's needs, various types of services with similar functionalities but different quality-of-service (QoS) criteria can be delivered, which often must be combined to meet the users' requests. The optimal selection and composition of these services are realized as an interesting issue. In this paper, we propose a moth-flame optimization (MFO) algorithm, which is a novel nature-inspired metaheuristic paradigm for the web service composition (WSC) problem called "MFO-WSC," to improve the QoS criteria in the distributed cloud environment. Also, formal modeling is presented for the QoS-aware MFO-WSC algorithm with the model checking approach that receives the particular benefits to collaborate the correctness of the proposed algorithm. The correctness of the proposed behavior model is examined using some logical problems such as deadlock-free, fairness, and reachability conditions in the new symbolic model verifier model checker. The experimental results indicate the effectiveness of the proposed algorithm in comparison with similar related works.

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“…4) Cuckoo Search: Cuckoo search (CS) is an optimization algorithm developed by Doolan et al [40]. CS also has been utilized in solving QoS-aware service composition [32].…”

Section: Algorithm Comparisonmentioning

confidence: 99%

Toward Risk Reduction for Mobile Service Composition

Deng

Huang

Liu

et al. 2016

IEEE Trans. Cybern.

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The advances in mobile technologies enable us to consume or even provide services through powerful mobile devices anytime and anywhere. Services running on mobile devices within limited range can be composed to coordinate together through wireless communication technologies and perform complex tasks. However, the mobility of users and devices in mobile environment imposes high risk on the execution of the tasks. This paper targets reducing this risk by constructing a dependable service composition after considering the mobility of both service requesters and providers. It first proposes a risk model and clarifies the risk of mobile service composition; and then proposes a service composition approach by modifying the simulated annealing algorithm. Our objective is to form a service composition by selecting mobile services under the mobility model and to ensure the service composition have the best quality of service and the lowest risk. The experimental results demonstrate that our approach can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.

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Cuckoo-inspired hybrid algorithm for selecting the optimal Web service composition

Cited by 12 publications

References 8 publications

Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

Hybrid Metaheuristics for QoS-Aware Service Composition: A Systematic Mapping Study

A moth‐flame optimization algorithm for web service composition in cloud computing: Simulation and verification

Toward Risk Reduction for Mobile Service Composition

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