Abstract

This paper addresses the problem of optimality in semantic Web service composition by proposing a hybrid nature-inspired method for selecting the optimal or near-optimal solution in semantic Web Service Composition. The method hybridizes the Honey-Bees Mating Optimization algorithm with components inspired from genetic algorithms, reinforcement learning, and tabu search. To prove the necessity of hybridization, we have analyzed comparatively the experimental results provided by our hybrid selection algorithm versus the ones obtained with the classical Honey Bees Mating Optimization algorithm and with the genetic-inspired algorithm of Canfora et al.

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Cite this article

@article{cai-3169,
  author  = {Viorica Rozina Chifu and Cristina Bianca Pop and Ioan Salomie and Emil Stefan Chifu},
  title   = {Hybrid Honey Bees Mating Optimization Algorithm for Identifying the Near-Optimal Solution in Web Service Composition},
  journal = {Computing and Informatics},
  volume  = {36},
  number  = {5},
  pages   = {1143--1172},
  year    = {2017},
  issn    = {1335-9150},
  doi     = {10.4149/cai_2017_5_1143}
}
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