This paper presents a bio-inspired hybrid algorithm that selects the optimal solution in semanticWeb service composition. The proposed algorithm combines the cuckoo search metaheuristic with evolutionary computing, reinforcement learning and tabu search to optimize the selection process in terms of execution time and explored search space. We model the search space as an Enhanced Planning Graph structure which encodes all the possible composition solutions for a given user request. To establish whether a solution is optimal, the QoS attributes of the services involved in the composition as well as the semantic similarity between them are considered as evaluation criteria. The hybrid selection algorithm has been evaluated on a set of scenarios from the trip planning domain and comparatively analyzed with a tabu search-based selection algorithm.
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