Two-Step Artificial Bee Colony Algorithm Enhancement for QoS-Aware Web Service Selection Problem

Dahan, Fadl; Mathkour, Hassan; Arafah, Mohammed Amer

doi:10.1109/access.2019.2894683

Cited by 30 publications

(39 citation statements)

References 16 publications

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“…Let n equal the number of tasks and m equal the number of WSs that can be used to achieve a given task. In fact, the value of m varies but for simplicity in this study the variable is fixed for each dataset i.e., in the experiment each dataset contains a special workflow with a fixed value for m. Figure 1 shows a hypothetical workflow of tasks with corresponding WSs [7,9,10]. The challenge of finding the best combination of WSs is a combinatorial optimization problem.…”

Section: Problem Definition and Related Workmentioning

confidence: 99%

An Enhanced Ant Colony Optimization Based Algorithm to Solve QoS-Aware Web Service Composition

et al. 2021

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Web Service Composition (WSC) can be defined as the problem of consolidating the services regarding the complex user requirements. These requirements can be represented as a workflow. This workflow consists of a set of abstract task sequence where each sub-task represents a definition of some user requirements. In this work, we propose a more efficient neighboring selection process and multi-pheromone distribution method named Enhanced Flying Ant Colony Optimization (EFACO) to solve this problem. The WSC problem has a challenging issue, where the optimization algorithms search the best combination of web services to achieve the functionality of the workflow's tasks. We aim to improve the computation complexity of the Flying Ant Colony Optimization (FACO) algorithm by introducing three different enhancements. We analyze the performance of EFACO against six of existing algorithms and present a summary of our conclusions.

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Section: Problem Definition and Related Workmentioning

confidence: 99%

An Enhanced Ant Colony Optimization Based Algorithm to Solve QoS-Aware Web Service Composition

et al. 2021

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“…In [37], the author proposed an enhanced Artificial Bee Colony(ABC) algorithm for solving the QWSC. This method improves the performance of ABC on QWSC problems through adaptive neighborhood selection and replacement strategies.…”

Section: Meta-heuristic Algorithmsmentioning

confidence: 99%

A Meta-Heuristic-Based Approach for Qos-Aware Service Composition

Chen

2020

IEEE Access

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Recently, with the rapid increase in the number of web services, QoS-aware Web Service Composition(QWSC) has become a popular topic in both industry and academia. Meta-heuristic algorithm, as an effective way to solve classical optimization problems, has been successfully applied to QWSC nowdays. However, such approach has intrinsic drawbacks and usually lack of good performance in large-scale scenarios. For example, some meta-heuristic algorithms are suitable for continuous search space, while the search space of QWSC is discrete. For solving those problems which were commonly faced when applying meta-heuristic algorithm on QWSC, in this research, we firstly introduce a preprocessing approach for constructing fuzzy continuous neighborhood relations of concrete services, which makes the local search strategy of meta-heuristic algorithms be as effective in discrete space as in continuous space, thus improving the optimization performance. Second, we combine Harris Hawks Optimization (HHO) algorithm and logical chaotic sequence to propose an improved meta-heuristic algorithm named CHHO for solving QWSC. The ergodic and chaotic characteristics of chaotic sequences are used to enhance the ability of the CHHO to jump out of the local optimum for further optimization. Experimental results show that the CHHO has better optimization performance by comparing with the existing mainstream algorithms when solving QWSC problems. Additionally, the preprocessing approach not only greatly improves the optimization performance of the CHHO but also can be freely utilized in other meta-heuristics based approaches. INDEX TERMS Meta-heuristic algorithm, QoS-aware web service composition, Harris hawks optimization, fuzzy continuous, logical chaotic sequence.

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“…Xu et al [31] proposed a chaotic turbo particle swarm optimization algorithm based on a predatory search to obtain candidate services. Ghobaei-Arani et al [32] and Dahan et al [33] proposed the cuckoo algorithm and the artificial bee colony algorithm to search for the best combination of web services.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Microservice Composition Based on Artificial Immune Algorithm Considering Fuzziness and User Preference

et al. 2020

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Microservices is a new paradigm in cloud computing that separates traditional monolithic applications into groups of services. These individual services may correlate or cross multi-clouds. Compared to a monolithic architecture, microservices are faster to develop, easier to deploy, and maintain by leveraging modern containers or other lightweight virtualization. To satisfy the requirements of end-users and preferences, appropriate microservices must be selected to compose complicated workflows or processes from within a large space of candidate services. The microservice composition should consider several factors, such as user preference, correlation effects, and fuzziness. Due to this problem being NP-hard, an efficient metaheuristic algorithm to solve large-scale microservice compositions is essential. We describe a microservice composition problem for multi-cloud environments that considers service grouping relations and corresponding correlation effects of the service providers within intra-or inter-clouds. We use the triangular fuzzy number to describe the uncertainty of QoS attributes, the improved fuzzy analytic hierarchy process to calculate multi-attribute QoS, construct fuzzy weights related to user preferences, and transform the multi-optimal problem into a single-optimal problem. We propose a new artificial immune algorithm based on the immune memory clone and clone selection algorithms. We also introduce several optimal strategies and conduct numerical experiments to verify effects and efficiencies. Our proposed method combines the advantages of monoclone, multi-clone, and co-evolution, which are suitable for the large-scale problems addressed in this paper. INDEX TERMS Fuzzy analytic hierarchy process (FAHP), microservice, microservice group, quality of service (QoS), QoS attribute, the parallel cooperative short-term memory injection multi-clone clonal selection algorithm (ParaCoSIMCSA).

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Two-Step Artificial Bee Colony Algorithm Enhancement for QoS-Aware Web Service Selection Problem

Cited by 30 publications

References 16 publications

An Enhanced Ant Colony Optimization Based Algorithm to Solve QoS-Aware Web Service Composition

An Enhanced Ant Colony Optimization Based Algorithm to Solve QoS-Aware Web Service Composition

A Meta-Heuristic-Based Approach for Qos-Aware Service Composition

Optimization of Microservice Composition Based on Artificial Immune Algorithm Considering Fuzziness and User Preference

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