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.
A New Multi-objective Evolutionary Algorithm for Inter-Cloud Service Composition
