A collaborative service group-based fuzzy QoS-aware manufacturing service composition using an extended flower pollination algorithm

2019 ◽  
Vol 95 (4) ◽  
pp. 3091-3114 ◽  
Author(s):  
Shuai Zhang ◽  
Wenting Yang ◽  
Wenyu Zhang ◽  
Mingzhou Chen
Keyword(s):  
Service Composition ◽  
Flower Pollination Algorithm ◽  
Flower Pollination ◽  
Service Group ◽  
Manufacturing Service Composition ◽  
Extended Flower Pollination Algorithm

scholarly journals A New Manufacturing Service Selection and Composition Method Using Improved Flower Pollination Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wenyu Zhang ◽  
Yushu Yang ◽  
Shuai Zhang ◽  
Dejian Yu ◽  
Yangbing Xu
Keyword(s):  
Multiobjective Optimization ◽  
Solution Space ◽  
Service Selection ◽  
Flower Pollination Algorithm ◽  
Environmental Index ◽  
Flower Pollination ◽  
De Algorithm ◽  
Mutation Operators ◽  
Skyline Operator ◽  
Manufacturing Service Composition

With an increasing number of manufacturing services, the means by which to select and compose these manufacturing services have become a challenging problem. It can be regarded as a multiobjective optimization problem that involves a variety of conflicting quality of service (QoS) attributes. In this study, a multiobjective optimization model of manufacturing service composition is presented that is based on QoS and an environmental index. Next, the skyline operator is applied to reduce the solution space. And then a new method called improved Flower Pollination Algorithm (FPA) is proposed for solving the problem of manufacturing service selection and composition. The improved FPA enhances the performance of basic FPA by combining the latter with crossover and mutation operators of the Differential Evolution (DE) algorithm. Finally, a case study is conducted to compare the proposed method with other evolutionary algorithms, including the Genetic Algorithm, DE, basic FPA, and extended FPA. The experimental results reveal that the proposed method performs best at solving the problem of manufacturing service selection and composition.

A Hybrid Hyper-heuristic Flower Pollination Algorithm for Service Composition Problem in IoT

2019 ◽  
Vol 13 ◽  
Author(s):  
Neeti Kashyap ◽  
A. Charan Kumari ◽  
Rita Chhikara
Keyword(s):  
Evolutionary Algorithm ◽  
Service Composition ◽  
Convergence Rates ◽  
Modern Science ◽  
Optimization Methods ◽  
Search Space ◽  
Flower Pollination Algorithm ◽  
The Novel ◽  
Flower Pollination ◽  
Composition Problem

Objectives: The modern science applications have non-continuous and multivariate nature due to which the traditional optimization methods suffer a lack of efficiency. Flower pollination is a natural interesting procedure in the real world. The novel optimization algorithms can be designed by employing the evolutionary capability of the flower pollination to optimize resources. Method: This paper introduces the hybrid algorithm named Hybrid Hyper-heuristic Flower Pollination Algorithm, HHFPA. It uses a combination of flower pollination algorithm (FPA) and Hyper-heuristic evolutionary algorithm (HypEA). This paper compares the basic FPA with the proposed algorithm named HHFPA. FPA is inspired by the pollination process of flowers whereas the hyper-heuristic evolutionary algorithm operates on the heuristics search space that contains all the heuristics to find a solution for a given problem. The proposed algorithm is implemented to solve the Quality of Service (QoS) based service composition Problem (SCoP) in Internet of Things (IoT). With increasing services with same functionality on the web, selecting a suitable candidate service based on non-functional characteristics such as QoS has become an inspiration for optimization. Results: This paper includes experimental results showing better outcomes to find the best solution using the proposed algorithm as compared to Basic FPA. Conclusion: The empirical analysis also reveals that HHFPA outperformed basic FPA in solving the SCoP with more convergence rates.

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