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Abstract. This paper presents a hierarchical and easy configurable framework for the implementation of distributed evolutionary algorithms for multiobjective optimization problems. The proposed approach is based on a layered structure corresponding to different execution environments like single computers, computing clusters and grid infrastructures. Two case studies, one based on a classical test suite in multiobjective optimization and one based on a data mining task, are presented and the results obtained both on a local cluster of computers and in a grid environment illustrates the characteristics of the proposed implementation framework.

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SUMMARYIn the growing market of cloud computing dominated by proprietary solutions, the adoption of open-source and deployable middleware that hides the service heterogeneity and ensure code portability can provide important benefits for the fast development of new services. Therefore, this paper exposes the mechanisms for orchestrating cloud-enabled hardware and software resources supported by a recently developed open-source platform as a service.

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