An interval-based multi-objective artificial bee colony algorithm for solving the web service composition under uncertain QoS

Seghir, Fateh; Khababa, Abdallah; Semchedine, Fouzi

doi:10.1007/s11227-019-02814-9

Cited by 24 publications

(9 citation statements)

References 45 publications

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“…The curves shown in Figure 8 present the evolution of the computation time. We can notice that our approach is sensitive to the number of instances and slower than the approaches, 48,49 this is mainly due to the computation of GQC that is based on the number of instances l and number of abstract tasks n.…”

Section: Experimental Studymentioning

confidence: 98%

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Web service selection and composition based on uncertain quality of service

Yasmina

Fethallah

Fadoua

2021

Concurrency and Computation

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Web services are becoming a major utility for accomplishing complex tasks over the Internet. In practice, the end‐users usually search for Web service compositions that best meet the quality of service (QoS) requirements (i.e., QoS global constraints). Since the number of services is constantly increasing and their respective QoS is inherently uncertain (due to environmental conditions), the task of selecting optimal compositions becomes more challenging. To tackle this problem, we propose a heuristic based on majority judgment that allows for reducing the search space. In addition, we perform a constraint programming search to select the Top K compositions that fulfill the QoS global constraints. The experimental results demonstrate the high performance of our approach.

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Section: Experimental Studymentioning

confidence: 98%

“…In Reference 48, the authors leverage an interval‐based multiobjective artificial bee colony approach to solve the uncertain QoS‐aware service composition problem. This approach integrates a new uncertain constrained dominance relation to handle the interval‐valued objective functions.…”

Section: State‐of‐the‐artmentioning

confidence: 99%

Web service selection and composition based on uncertain quality of service

Yasmina

Fethallah

Fadoua

2021

Concurrency and Computation

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“…The search procedure supports the exploration of the ABC, while exploitation is enhanced by differential evolution. Seghir et al [18] proposed a novel interval-based method to enhance the ABC's performance. A neighborhood selection method was subsequently proposed to enhance ABC exploitation.…”

Section: Related Workmentioning

confidence: 99%

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

Dahan¹,

Alwabel²

2023

Intelligent Automation &Amp; Soft Computing

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In recent years, cloud computing has provided a Software As A Service (SaaS) platform where the software can be reused and applied to fulfill complicated user demands according to specific Quality of Services (QoS) constraints. The user requirements are formulated as a workflow consisting of a set of tasks. However, many services may satisfy the functionality of each task; thus, searching for the composition of the optimal service while maximizing the QoS is formulated as an NP-hard problem. This work will introduce a hybrid Artificial Bee Colony (ABC) with a Cuckoo Search (CS) algorithm to untangle service composition problem. The ABC is a well-known metaheuristic algorithm that can be applied when dealing with different NP-hard problems with an outstanding record of performance. However, the ABC suffers from a slow convergence problem. Therefore, the CS is used to overcome the ABC's limitations by allowing the abandoned bees to enhance their search and override the local optimum. The proposed hybrid algorithm has been tested on 19 datasets and then compared with two standard algorithms (ABC and CS) and three state-of-the-art swarm-based composition algorithms. In addition, extensive parameter study experiments were conducted to set up the proposed algorithm's parameters. The results indicate that the proposed algorithm outperforms the standard algorithms in the three comparison criteria (best fitness value, average fitness value, and average execution time) overall datasets in 30 different runs. Furthermore, the proposed algorithm also exhibits better performance than the state-of-the-art algorithms in the three comparison criteria over 30 different runs.

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“…Novel QoS modeling, including interval number multiobjective [161], and fuzzy ranking method with fuzzy numbers introduced [162], and ameliorated with artificial bee colony to achieve better results. Zhou and Yao [163] proposed a cuckoo search with a levy flight operator to improve global exploration capability.…”

Section: Classification Of Hybrid Metaheuristicmentioning

confidence: 99%

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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An interval-based multi-objective artificial bee colony algorithm for solving the web service composition under uncertain QoS

Cited by 24 publications

References 45 publications

Web service selection and composition based on uncertain quality of service

Web service selection and composition based on uncertain quality of service

Artificial Bee Colony with Cuckoo Search for Solving Service Composition

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

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