An Adapted Ant-Inspired Algorithm for Enhancing Web Service Composition

Dahan, Fadl; Hindi, Khalil El; Ghoneim, Ahmed

doi:10.4018/978-1-5225-7501-6.ch049

Cited by 4 publications

(19 citation statements)

References 24 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%

“…The number of available combinations (solutions) for this problem is exponential which makes the challenge of finding the best solution an NP-hard problem [6]. In [7], a Flying Ant Colony Optimization (FACO algorithm has been proposed, based on Ant Colony Optimization (ACO) [8]. However, it incorporates imaginary flying ants which inject pheromones from a distance.…”

Section: Introductionmentioning

confidence: 99%

“…In this case, the best ants share the amount of pheromones with their neighboring nodes. Although FACO enhances ACO in terms of solution quality, but it increases the execution time [7] because it must determine neighboring nodes. Multi-objective optimization problems are difficult to solve directly because of the discrepancies in the objectives and the lack of efficient tools for solving such problems [2].…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…The other recent algorithm is Flying Ant Colony Optimization (FACO) which works with a combination of walking and flying ants' functions. This algorithm aimed for preserving the equilibrium of exploitation and exploration [10]. Figure 1 illustrates the process of the Dynamic Flying Ant Colony Optimization DFACO [11] algorithm which is modified from FACO and is added to the process of ACO.…”

Section: Variation Of Improved Aco Algorithm To Avoid the Problem Of mentioning

confidence: 99%

“…where DC is DynamicPheromone_ConvergenceSpeed with the value in the range of [1][2][3][4][5][6][7][8][9][10]. E. pheromone injection: Δ k (r, s) If the node/vehicle receives pheromone from flying ants, then pheromone injection applies on the path by using the following equation.…”

Section: M|mentioning

confidence: 99%

Design and performance evaluation of Improved DFACO protocol based on dynamic clustering in VANETs

Ebadinezhad

2021

SN Appl. Sci.

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This study focuses on Vehicular Ad-hoc Networks (VANETs) stability in an environment that is dynamic which often leads to major challenges in VANETs, such as dynamic topology changes, shortest routing paths and also scalability. One of the best solutions for such challenges is clustering. In this study, we present five novel routing protocols based on Dynamic Flying Ant Colony Optimization (DFACO) algorithm to achieve minimum number of clusters, high accuracy, minimum time and solution cost by selecting the best cluster-head which is obtained from a new mechanism of dynamic metaheuristic-based clustering. In this regard, major improvements are applied on classical DFACO by adjusting the procedure for updating the pheromone and tuning the evaporation rate that has a major role in DFACO. In this research two individual phases of experiments are conducted for performance evaluation of proposed routing protocols. The presented solution is verified and compared to classic Ant Colony Optimization (ACO), DFACO and ACO Based Clustering Algorithm for VANET (CACONET) algorithms in phase one; and compared to clustering algorithms such as Center Position and Mobility CPM), Highest-Degree algorithm (HD), Angle-based Clustering Algorithm (ACA) in phase two through NS-2 and SUMO simulation tools. Simulation results have confirmed the expected behaviour and show that our proposed protocols achieve better node connectivity and cluster stability than the former.

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Enhancement of Ant Colony Optimization for QoS-Aware Web Service Selection

et al. 2019

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In service-oriented computing, web services composition is the process of translating user requirements into a workflow. This workflow comprises many tasks, each of which includes an abstract definition for some of the user requirements. Web services can be aggregated to handle the workflow. Many of these services are available from various providers for each task; they are referred to, in aggregate, as the candidate list. The web service selection (WSS) problem centers on selecting the best service from these candidates based on the quality of service (QoS) features. In this paper, we propose an enhancement to the ant colony optimization (ACO) algorithm based on a swap concept for the QoS-aware WSS problem. The aim of the enhancement to the ACO is to avoid the trap of local optima and reduce the search duration. We believe that the integration of many potent solutions will help the ACO algorithm yield a better solution and avoid stagnation. Several experiments were conducted to compare the proposed algorithm with the ACO and flying ACO (FACO) algorithms. Two different types of experiments using 22 datasets were done with 30 independent repetitions. The first type of experiment's results shows that the proposed algorithm is better than ACO by 12% and FACO by 11% in terms of quality of solutions. The results in the second type of experiment show that the proposed algorithm continuously outperforms both algorithms in terms of quality of solutions.INDEX TERMS Service-oriented computing (SOC), web services composition (WSC), web service (WS), web service selection (WSS), ant colony optimization (ACO).

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An Adapted Ant-Inspired Algorithm for Enhancing Web Service Composition

Cited by 4 publications

References 24 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

Design and performance evaluation of Improved DFACO protocol based on dynamic clustering in VANETs

Enhancement of Ant Colony Optimization for QoS-Aware Web Service Selection

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