Correlation-aware manufacturing service composition model using an extended flower pollination algorithm

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.

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

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191220152741 ◽

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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