BAT and Hybrid BAT Meta-Heuristic for Quality of Service-Based Web Service Selection

Abstract Efficient QoS-based service selection from a pool of functionally substitutable web services (WS) for constructing composite WS is important for an efficient business process. Service composition based on diverse QoS requirements is a multi-objective optimization problem. Meta-heuristic techniques such as genetic algorithm (GA), particle swarm optimization (PSO), and variants of PSO have been extensively used for solving multi-objective optimization problems. The efficiency of any such meta-heuristic techniques lies with their rate of convergence and execution time. This article evaluates the efficiency of BAT and Hybrid BAT algorithms against the existing GA and Discrete PSO techniques in the context of service selection problems. The proposed algorithms are tested on the QWS data set to select the best fit services in terms of maximum aggregated end-to-end QoS parameters. Hybrid BAT is found to be efficient for service composition.

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