Hybridization of cuckoo search and firefly algorithms for selecting the optimal solution in semantic web service composition

Abstract

This chapter investigates how the Cuckoo Search and Firefly Algorithm can be hybridized for performance improvement in the context of selecting the optimal or near-optimal solution in semantic Web service composition. Cuckoo Search and Firefly Algorithm are hybridized with genetic, reinforcement learning and tabu principles to achieve a proper exploration and exploitation of the search process. The hybrid algorithms are applied on an enhanced planning graph which models the service composition search space for a given user request. The problem of finding the optimal solution encoded in the enhanced planning graph can be reduced to identifying a configuration of semantic Web services, out of a very large set of possible configurations, which maximizes a fitness function which considers semantics and QoS attributes as selection criteria. To analyze the benefits of hybridization we have comparatively evaluated the classical Cuckoo Search and Firefly Algorithms versus the proposed hybridized algorithms. © 2014 Springer International Publishing Switzerland.