An Orthogonal Genetic Algorithm for QoS-Aware Service Composition

Service composition has been proven to be a convincing computing paradigm for rapidly constructing large-scale distributed applications within and across organizational boundaries. Quality of service (QoS)-aware service composition, i.e. selection of the optimal execution plan that maximizes the composition’s end-to-end QoS properties, is an active area of research and development endeavors in service composition. In this article, we propose an orthogonal genetic algorithm (OGA) for QoSaware service composition problem. Its significant feature is to incorporate an orthogonal design method into the initial population generation process and crossover operation. As a result, our algorithm is more robust and can search the solution space in a statistically sound manner. We have executed the OGA to solve 81 randomly generated service composition problems with different sizes and structures based on QWS data set including 2507 real Web services. The results indicate that our OGA can find near-optimal solutions within moderate numbers of generation and has the performance superiority in comparison with many existing optimization algorithms.

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