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Applying Evolutionary Many-Objective Optimization Algorithms to the Quality-Driven Web Service Composition Problem
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Author(s): Arion de Campos Jr. (State University of Ponta Grossa, Brazil), Aurora T. R. Pozo (Federal University of Parana, Brazil)and Silvia R. Vergilio (Federal University of Parana, Brazil)
Copyright: 2016
Pages: 25
Source title:
Automated Enterprise Systems for Maximizing Business Performance
Source Author(s)/Editor(s): Petraq Papajorgji (Universiteti Europian i Tiranes, Albania), François Pinet (National Research Institute of Science and Technology for Environment and Agriculture, France), Alaine Margarete Guimarães (State University of Ponta Grossa, Brazil)and Jason Papathanasiou (University of Macedonia, Greece)
DOI: 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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