Abstract
This paper presents an accelerated Promethee (Preference Ranking Organization METHod for Enrichment Evaluations) multi-criteria algorithm based on dimensionality reduction in large scale environments. In our context, the Promethee algorithm is used to select from a large set of objects, one or a small set of objects with a good compromise between several qualitative and quantitative criteria. The exact solution can be used by applying the exact multi-criteria Promethee algorithm. However, the drawback, with this type of exact algorithm, is the long execution time due to the combinatorial aspect of the problem. The exact Promethee computing time is linked both to the dimension of the problem (number of qualitative and quantitative criteria) and the size of the problem (number of objects). To address the previous drawback, we propose to accelerate the Promethee algorithm in combining the exact Promethee algorithm with an algorithm inherited from the Machine Learning (ML) field. The experiments demonstrate the potential of our approach under different scenarios to accelerate the respond time.
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Acknowledgments
We thank the Grid5000 team for their help to use the testbed. Grid’5000 is supported by a scientific interest group (GIS) hosted by Inria and including CNRS, RENATER and several universities as well as other organizations.
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Menouer, T., Cérin, C., Darmon, P. (2020). Accelerated Promethee Algorithm Based on Dimensionality Reduction. In: Hsu, CH., Kallel, S., Lan, KC., Zheng, Z. (eds) Internet of Vehicles. Technologies and Services Toward Smart Cities. IOV 2019. Lecture Notes in Computer Science(), vol 11894. Springer, Cham. https://doi.org/10.1007/978-3-030-38651-1_17
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DOI: https://doi.org/10.1007/978-3-030-38651-1_17
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