2015
DOI: 10.1007/978-3-319-28658-7_34
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A Genetic PSO Algorithm with QoS-Aware Cluster Cloud Service Composition

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Cited by 12 publications
(4 citation statements)
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“…Nevertheless, in a premutation based service composition in cloud's Infrastructure as a Service layer (IaaS), Mistry et al [79] proposed a hybrid adaptive genetic algorithm with novel QoS modelling. Furthermore, Faruk et al [124] presented a hybrid GA-PSO as an improvement strategy to acquire convergence intensity. A large body of evidence suggests that operator modification in metaheuristics has been the most used approach to improving algorithm performance.…”
Section: Classification Of Hybrid Metaheuristicmentioning
confidence: 99%
“…Nevertheless, in a premutation based service composition in cloud's Infrastructure as a Service layer (IaaS), Mistry et al [79] proposed a hybrid adaptive genetic algorithm with novel QoS modelling. Furthermore, Faruk et al [124] presented a hybrid GA-PSO as an improvement strategy to acquire convergence intensity. A large body of evidence suggests that operator modification in metaheuristics has been the most used approach to improving algorithm performance.…”
Section: Classification Of Hybrid Metaheuristicmentioning
confidence: 99%
“…Their solution achieves a preferable trade‐off between time and quality, and it is an applied solution for deploying in distributed cloud environments. Faruk et al proposed a unique heuristic algorithm to decide the QoS‐aware cloud service selection using an enhanced genetic PSO algorithm, which is broadly employed to crack hefty‐scale optimization issues. Also, the adaptive nonuniform mutation approach to achieve the best particle globally to boost the population classification on the motivation of conquering the precocity level of the genetic PSO algorithm is presented.…”
Section: Related Workmentioning
confidence: 99%
“…To evaluate the proposed algorithm, we consider five various numbers of services (10, 30, 50, 70, and 90) and also eight different numbers of atomic services for each set (200, 300, 400, 500, 600, 700, 800, and 900) in the form of five different scenarios according to Table . We compared the proposed algorithm with the most effective WSC algorithms as follows: a cuckoo search algorithm for web service composition in the geographically distributed cloud environments (CSA‐WSC), a genetic‐based search algorithm that considers network delay (GS‐S‐Net), a genetic particle swarm optimization algorithm for web service composition (GAPSO‐WSC) is used for discovering optimum regions from complex search spaces via the collaboration of individuals in a crowd of particles, and a greedy‐based algorithm for web service composition (Greedy). In the Greedy algorithm, the user's region is first determined.…”
Section: Performance Evaluationmentioning
confidence: 99%
“…Rodriguez-Mier et al 35 proposed a hybrid approach for automatic composition of web services that generated semantic input-output matching compositions minimizing the number of services and optimizing the global QoS. Faruk et al 36 proposed an adaptive non-uniform mutation approach for QoSaware cloud service composition to attain the best particle globally to boost the population assortment on the motivation of conquering the prematurity level of genetic particle swarm optimization algorithm. The above research works focus on the optimization of service composition, having service quality support function, but they lack semantic support function, having poor service composition connectivity, and being difficult to verify the attributes of service composition.…”
Section: Literature Reviewmentioning
confidence: 99%