Self-adaptive learning for hybrid genetic algorithms

El-Mihoub, Tarek A.; Hopgood, Adrian A.; Nolle, Lars

doi:10.1007/s12065-020-00425-5

Cited by 8 publications

(2 citation statements)

References 27 publications

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“…Comparing the results of the models based on the optimization algorithms shows that the error of the COA is generally lower than the other two algorithms. This result is consistent with the poor performance of GA and PSO in local search (Zhuo and Yu 2019;El-Mihoub et al 2021) and maintaining a balance between the local and global search of the COA (Mareli and Twala 2018).…”

Section: Developing and Evaluating Permeability Prediction Modelssupporting

confidence: 85%

Prediction of permeability of highly heterogeneous hydrocarbon reservoir from conventional petrophysical logs using optimized data-driven algorithms

Sheykhinasab

Mohseni

Bahari³

et al. 2022

J Petrol Explor Prod Technol

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Permeability is an important parameter in the petrophysical study of a reservoir and serves as a key tool in the development of an oilfield. This is while its prediction, especially in carbonate reservoirs with their relatively lower levels of permeability compared to sandstone reservoirs, is a complicated task as it has larger contributions from heterogeneously distributed vugs and fractures. In this respect, the present research uses the data from two wells (well A for modeling and well B for assessing the generalizability of the developed models) drilled into a carbonate reservoir to estimate the permeability using composite formulations based on least square support vector machine (LSSVM) and multilayer extreme learning machine (MELM) coupled with the so-called cuckoo optimization algorithm (COA), particle swarm optimization (PSO), and genetic algorithm (GA). We further used simple forms of convolutional neural network (CNN) and LSSVM for the sake of comparison. To this end, firstly, the Tukey method was applied to identify and remove the outliers from modeling data. In the next step, the second version of the nondominated sorting genetic algorithm (NSGA-II) was applied to the training data (70% of the entire dataset, selected randomly) to select an optimal group of features that most affect the permeability. The results indicated that although including more input parameters in the modeling added to the resultant coefficient of determination (R2) while reducing the error successively, yet the slope of the latter reduction got much slow as the number of input parameters exceeded 4. In this respect, petrophysical logs of P-wave travel time, bulk density, neutron porosity, and formation resistivity were identified as the most effective parameters for estimating the permeability. Evaluation of the results of permeability modeling based on root-mean-square error (RMSE) and R2 shed light on the MELM-COA as the best-performing model in the training and testing stages, as indicated by (RMSE = 0.5600 mD, R2 = 0.9931) and (RMSE = 0.6019 mD, R2 = 0.9919), respectively. The generalizability assessment conducted on the prediction of permeability in well B confirmed the MELM-COA can provide reliable permeability predictions by achieving an RMSE of 0.9219 mD. Consequently, the mentioned methodology is strongly recommended for predicting the permeability with high accuracy in similar depth intervals at other wells in the same field should the required dataset be available.

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Section: Developing and Evaluating Permeability Prediction Modelssupporting

confidence: 85%

Prediction of permeability of highly heterogeneous hydrocarbon reservoir from conventional petrophysical logs using optimized data-driven algorithms

Sheykhinasab

Mohseni

Bahari³

et al. 2022

J Petrol Explor Prod Technol

View full text Add to dashboard Cite

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“…Algoritma genetika juga digunakan untuk klasifikasi serta pengoptimalan lainnya [10]. Kinerja algoritma genetika berkerja dalam bentuk kode sekumpulan parameter [11], Untuk menyelesaikan masalah yang lebih rumit, algoritma genetika terintegrasi menggunakan metode hibridisasi untuk meningkatkan efektivitas kinerjanya [12]. Pencarian dilakukan dengan populasi dari masalah penjadwalan di representasikan menjadi string kromosom [13,14] serta berinterkasi dalam sub komponen [15] .…”

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Implementasi Algoritma Genetika untuk Aplikasi Penjadwalan Sistem Kerja Shift

Priatna

Warta²,

Sulistiyo³

2023

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Perusahaan yang beroperasi jam kerja 24 jam mempunyai tugas berat dalam menentukan jadwal shift kerja. Selain memberikan layanan terbaik untuk pelanggan setiap waktu, dan memiliki segmen pasar khusus. Untuk melakukan produktivitas optimal diperlukan jadwal shift kerja yang teratur bagi perusahaan. Tujuan penelitian ini adalah memecahkan permasalahan pengelolaan jadwal kerja menggunakan algoritma genetika disalah satu perusahaan konektivitas penyedia jaringan internet yang secara manual membuat jadwal dengan bantuan microsoft excel dengan pertimbangan bahwa setiap karyawan memiliki shift kerja berbeda untuk setiap bulannya. Aplikasi ini menggunakan Unified Modeling Language sebagai model dari sistem penjadwalan shift kerja yang bertujuan untuk pengembangan sistem bisa dilakukan secara maksimal. Hasil dari implementasi perhitungan algoritma genetika menggunakan variabel waktu shift, variabel hari, variabel nama karyawan tidak ditemukan permasalahan penjadwalan shift kerja begitu juga setelah dirancang menggunakan aplikasi penjadwalan shift kerja muncul penjadwalan sistem secara otomatis dengan acuan permasalahan yang sudah bernilai value 0

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Optimized neuro fuzzy convolutional networks in gene expression analysis in microscopic image feature extraction and classification by deep learning architectures

Qamar

2024

Neural Comput & Applic

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Self-adaptive learning for hybrid genetic algorithms

Cited by 8 publications

References 27 publications

Prediction of permeability of highly heterogeneous hydrocarbon reservoir from conventional petrophysical logs using optimized data-driven algorithms

Prediction of permeability of highly heterogeneous hydrocarbon reservoir from conventional petrophysical logs using optimized data-driven algorithms

Implementasi Algoritma Genetika untuk Aplikasi Penjadwalan Sistem Kerja Shift

Optimized neuro fuzzy convolutional networks in gene expression analysis in microscopic image feature extraction and classification by deep learning architectures

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