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Applying Evolutionary Many-Objective Optimization Algorithms to the Quality-Driven Web Service Composition Problem

Applying Evolutionary Many-Objective Optimization Algorithms to the Quality-Driven Web Service Composition Problem

Arion de Campos Jr., Aurora T. R. Pozo, Silvia R. Vergilio
Copyright: © 2016 |Pages: 25
ISBN13: 9781466688414|ISBN10: 1466688416|EISBN13: 9781466688421
DOI: 10.4018/978-1-4666-8841-4.ch010
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MLA

de Campos Jr., Arion, et al. "Applying Evolutionary Many-Objective Optimization Algorithms to the Quality-Driven Web Service Composition Problem." Automated Enterprise Systems for Maximizing Business Performance, edited by Petraq Papajorgji, et al., IGI Global, 2016, pp. 170-194. https://doi.org/10.4018/978-1-4666-8841-4.ch010

APA

de Campos Jr., A., Pozo, A. T., & Vergilio, S. R. (2016). Applying Evolutionary Many-Objective Optimization Algorithms to the Quality-Driven Web Service Composition Problem. In P. Papajorgji, F. Pinet, A. Guimarães, & J. Papathanasiou (Eds.), Automated Enterprise Systems for Maximizing Business Performance (pp. 170-194). IGI Global. https://doi.org/10.4018/978-1-4666-8841-4.ch010

Chicago

de Campos Jr., Arion, Aurora T. R. Pozo, and Silvia R. Vergilio. "Applying Evolutionary Many-Objective Optimization Algorithms to the Quality-Driven Web Service Composition Problem." In Automated Enterprise Systems for Maximizing Business Performance, edited by Petraq Papajorgji, et al., 170-194. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-4666-8841-4.ch010

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Abstract

The Web service composition refers to the aggregation of Web services to meet customers' needs in the construction of complex applications. The selection among a large number of Web services that provide the desired functionalities for the composition is generally driven by QoS (Quality of Service) attributes, and formulated as a constrained multi-objective optimization problem. However, many equally important QoS attributes exist and in this situation the performance of the multi-objective algorithms can be degraded. To deal properly with this problem we investigate in this chapter a solution based in many-objective optimization algorithms. We conduct an empirical analysis to measure the performance of the proposed solution with the following preference relations: Controlling the Dominance Area of Solutions, Maximum Ranking and Average Ranking. These preference relations are implemented with NSGA-II using five objectives. A set of performance measures is used to investigate how these techniques affect convergence and diversity of the search in the WSC context.

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