Publications Internationales

Now showing 1 - 4 of 4

Vertical electrical sounding data inversion using continuous ant colony optimization algorithm: A case study from Hassi R’Mel, Algeria
(2022) Bouchaoui, Lyes; Ferahtia, Jalal; Farfour, Mohammed; Djarfour, Nouredine
Among the existing geophysical methods, the vertical electrical sounding remains a fast and economical way to detect groundwater resources. However, the interpretation of the vertical electrical sounding data often suffers from non-uniqueness due to the ill-posed nature of the inverse problem. In recent years, metaheuristic algorithms have been successfully used for solving ill-conditioned and ill-posed problems. This work presents a scheme that uses the continuous ant colony optimization (ACOR) technique to invert vertical electrical sounding data. The ACOR is a global search algorithm that explores and finds the globally optimal solution over a search space by mimicking the behaviour of biological ants. The development of this algorithm was due to the requirement to interpret a set of vertical electrical sounding collected at the region of Hassi R’Mel (Algerian Sahara). The area has a particular eological/geoelectrical structure, which renders the interpretation of vertical electrical sounding challenging as standard inversion approaches tend to fail to recover a reliable resistivity model. The ACOR algorithm was initially tested with synthetic data from models simulating the geological/hydrogeological structure of the studied area. The results verified the robustness and stability of the ACOR algorithm even in the presence of a high level of noise. Furthermore, the tests indicated that the ACOR algorithm performed better when compared to other inversion techniques for this particular geoelectrical structure. Five vertical electrical sounding profiles using a Schlumberger array collected in the region of Hassi R’Mel were inverted using the ACOR algorithm. The models confirmed the presence of the two central aquifer systems and showed the geometry of the aquifer with the most favourable conditions for water accumulations.
New criteria for wrapper feature selection to enhance bearing fault classification
(SAGE, 2023) Sahraoui, Mohammed Amine; Rahmoune, Chemseddine; Meddour, Ikhlas; Bettahar, Toufik; Zair, Mohamed
Classification is a critical task in many fields, including signal processing and data analysis. The accuracy and stability of classification results can be improved by selecting the most relevant features from the data. In this paper, a new criterion for feature selection using wrapper method is proposed, which is based on the evaluation of the classification results according to the accuracy and stability (standard deviation) of each class and the number of selected features. The pro- posed method is evaluated using Random Forest (RF) and Ant Colony Optimization (ACO) algorithms on a benchmark dataset. Results show that the proposed method outperforms classical feature selection methods in terms of accuracy and stability of classification results, especially for the difficult-to-classify combined damage class. This study demon- strates the effectiveness of the proposed new wrapper feature selection criterion to improve the performance of classifi- cation algorithms with higher stability (STD: C1 = 0.5, C2 = 0.8, C3 = 0.6, C4 = 1.8) and better accuracy (average C1 = 98.5%, C2 = 96.6%, C3 = 9.5%, C4 = 93) for the both; the statoric current and the vibration signal compared to other techniques. Machine learning methods had proven their efficiency in time-varying machines fault diagnosis when taking vibration signals and statoric currents extracted features as inputs. However, the use of the both demonstrated a higher robustness and a remarkable superiority.
Vertical electrical sounding data inversion using continuous ant colony optimization algorithm : a case study from Hassi R'Mel, Algeria
(John Wiley and Sons Inc, 2022) Bouchaoui, Lyes; Ferahtia, Jalal; Farfour, Mohammed; Djarfour, Nouredine
Among the existing geophysical methods, the vertical electrical sounding remains a fast and economical way to detect groundwater resources. However, the interpretation of the vertical electrical sounding data often suffers from non-uniqueness due to the ill-posed nature of the inverse problem. In recent years, metaheuristic algorithms have been successfully used for solving ill-conditioned and ill-posed problems. This work presents a scheme that uses the continuous ant colony optimization (ACOR) technique to invert vertical electrical sounding data. The ACOR is a global search algorithm that explores and finds the globally optimal solution over a search space by mimicking the behaviour of biological ants. The development of this algorithm was due to the requirement to interpret a set of vertical electrical sounding collected at the region of Hassi R'Mel (Algerian Sahara). The area has a particular geological/geoelectrical structure, which renders the interpretation of vertical electrical sounding challenging as standard inversion approaches tend to fail to recover a reliable resistivity model. The ACOR algorithm was initially tested with synthetic data from models simulating the geological/hydrogeological structure of the studied area. The results verified the robustness and stability of the ACOR algorithm even in the presence of a high level of noise. Furthermore, the tests indicated that the ACOR algorithm performed better when compared to other inversion techniques for this particular geoelectrical structure. Five vertical electrical sounding profiles using a Schlumberger array collected in the region of Hassi R'Mel were inverted using the ACOR algorithm. The models confirmed the presence of the two central aquifer systems and showed the geometry of the aquifer with the most favourable conditions for water accumulations
Optimization of WAG process using dynamic proxy, genetic algorithm and ant colony optimization
(Springer, 2018) Nait Amar, Menad; Zeraibi, Noureddine; Redouane, Kheireddine

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