Open AccessMCDM Filter with Pareto Parallel Implementation in Shared Memory Environment
Author(s) -
Loubna Lamrini,
Mohammed Chaouki Abounaima,
Fatima Zahra El Mazouri,
Mohammed Ouzarf,
Mohammed Talibi Alaoui
Publication year - 2022
Publication title -
statistics, optimization and information computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.297
H-Index - 12
eISSN - 2311-004X
pISSN - 2310-5070
DOI - 10.19139/soic-2310-5070-1216
Subject(s) - multiple criteria decision analysis , computer science , pareto principle , context (archaeology) , curse of dimensionality , process (computing) , filter (signal processing) , computation , multi objective optimization , big data , mathematical optimization , operations research , industrial engineering , data mining , machine learning , algorithm , mathematics , engineering , paleontology , computer vision , biology , operating system
Nowadays, multi-criteria decision-making (MCDM) methods are often used to solve problems involving large data sets, especially with the advent of the big data age. In such a context, the multi-criteria decision-making methods theoretically can be used but technically are not effificient in terms of the treatment time. Indeed, the majority of commercial or even experimental multi-criteria decision support tools always have limits in terms of the number of alternatives and the number of criteria to be retained in the decision-making process, which presents a computational challenge to relieve. This present paper discusses the application of parallel computation to meet this challenge and make the application of MCDM methods possible in the presence of a big number of alternatives and criteria. More precisely, the main objective of this work is to provide a parallel fifiltering mechanism that can be executed even on accessible personal computers and offering a short and reasonable response time. The introduction of a fifilter as a fifirst step in the decision-making process consists in retaining, as alternatives to be treated by the MCDM method, and by parallel processing only the Pareto solutions. To achieve this objective, we propose a parallel computing approach deploying the Open MP (Open Multi-Processing) paradigm on a shared memory environment to fifind Pareto solutions. To prove the effectiveness of the proposed approach for problems with large dimensionality, several numerical examples with different dimensions will be examined.