Particle Swarm Optimization Algorithm in Numerical Simulation of Saturated Rock Slope Slip

Liu, Bowen; Wang, Zhenwei; Zhong, Xiaoyong

doi:10.1155/2021/6682659

Cited by 6 publications

(5 citation statements)

References 16 publications

(19 reference statements)

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“…The example is as follows. Liu et al [22] used PSO to improve the accuracy of the rock slope slip simulation. Gao et al [23] combined PSO with Levy flight, also used in MPA, to solve high latency issues in the mobile cloud computing system.…”

Section: B Simulationmentioning

confidence: 99%

Partial Leader Optimizer

Kusuma,

Hasibuan

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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A new swarm intelligence-based metaheuristic optimizer, namely Partial Leader Optimizer (PLO), is presented. PLO contains several autonomous agents that represent the solution. The best solution represents collective intelligence, i.e., the leader. PLO has distinct mechanics in finding the acceptable solution during the given iteration. Every agent moves to a specified target in every iteration. Two options can be chosen to determine the target. First, the target is calculated by pushing the virtual best solution away from the corresponding agent. Second, the target is randomly chosen within the solution space. This target selection is conducted stochastically based on the threshold that is set manually before the iteration. Then, several candidates are generated between the target and the agent's current location. The distance between adjacent candidates is the same. The agent moves to the best candidate and updates the best solution. Simulation is implemented to observe and analyze the PLOâ€™s performance. The well-known 23 benchmark functions are used as the optimization problems. In this simulation, PLO is benchmarked with marine predator algorithm (MPA), particle swarm optimization (PSO), average subtraction-based optimizer (ASBO), slime mold algorithm (SMA), and pelican optimization algorithm (POA). The result shows that PLO is competitive compared to these algorithms, especially in solving fixed-dimension multimodal functions. PLO is better than PSO, MPA, SMA, ASBO, and POA in optimizing 22, 19, 18, 9, and 20 functions out of 23, respectively.

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Section: B Simulationmentioning

confidence: 99%

Partial Leader Optimizer

Kusuma,

Hasibuan

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Particle swarm Optimization (PSO) banyak digunakan di banyak bidang karena keunggulan operasinya yang sederhana dan kecepatan konvergensi yang cepat (Liu, Wang, & Zhong, 2021), Dengan asumsi bahwa ada m partikel dalam ruang solusi, ekspresi posisi dan kecepatan partikel ke-I dalam ruang n-dimensi ditunjukkan sebagai berikut: rumus:…”

Section: Particle Swarm Optimization (Pso)unclassified

Komparasi Machine Learning Berbasis Pso Untuk Prediksi Tingkat Keberhasilan Belajar Berbasis E-Learning

Saputra,

Nurajizah,

Maulidah

et al. 2023

JTIIK

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Perkembangan bidang teknologi memiliki aspek perkermbangan yang begitu cepat. penelitian kami memiliki tujuan untuk mentransmisikan sebuah pengetahuan tentang machine learning yang telah menjadi begitu popular digunakan hingga saat ini, pada penelitian ini bagaimana mendapatkan fitur seleksi atribut dan mendapatkan hasil prediksi dari pembelajaran pada Universitas atau lembaga Pendidikan yang menerapkan belajar dengan metode pembelajaran jarak jauh ataupun e-learning di era pandemic ini. Permasalahan pada penelitian ini yaitu jumlah atribut pada data dapat mengurangi akurasi, maka dari percobaan dengan beberapa algoritma pada machine learning kami mencoba menerapkan Particle Swarm Optimizatio(PSO) untuk meningkatkan akurasi yang lebih tinggi. Maka dari itu dapat disimpulkan penerapan menggunakan algoritma Naïve Bayes(NB) berbasis PSO mendapatkan hasil kenerja dengan bobot sebesar 94.40% dan angka AUC sebesar 94.50%, berikutnya Algoritma Support Vectore Machine(SVM) Berbasis PSO dengan hasil kinerja akurasi sebesar 88.20 dan nilai AUC seberar 91.10%, dan Artificial Neural Network(NN) berbasis Particle Swarm Optimizatio(PSO) menghasilkan skor hasil kinerja akurasi dengan bobot 99.20% dan nilai akurasi sebesar 98.50%, maka Artificial Neural Network(NN) berbasis PSO memiliki keunggulan lebih besar dari pada algoritma naïve bayer berbasis PSO dan Support Vector Machine(SVM) dengan PSO. Sedangkan atribut yang mempunyai pengaruh menentukan dari algoritma tersebut pada tingkat akurasi adalah Practice Questions, Quizzes, Midterm exams, dan Final exams. terbukti dari penelitian-penelitian kami yang sebelumnya maka algoritma neural network berbasis PSO memang memiliki keunggulan yang begitu baik. Karena ANN merupakan metode yang memiliki perhitungan yang membangun beberapa unit pada saat pemrosesan berdasarkan koneksitas yang saling berhubungan, metode ANN dengan akurasi prediksi dapat menjadi sebuah alat yang efisien dan baik untuk penelitian estimasi dan klasifikasi dalam bidang pendidikan. Abstract The development of the field of technology has a very fast development aspect. our research has the aim of transmitting knowledge about machine learning which has become so popularly used until now, in this study how to get attribute selection features and get predictive results from learning at universities or educational institutions that apply learning by distance learning methods or e-learning. -Learning in this pandemic era. The problem in this study is that the number of attributes in the data can reduce accuracy, so from experiments with several yahoos on machine learning, we tried to apply Particle Swarm Optimizatio (PSO) to increase higher accuracy. Then the application key using the PSO-based Naïve Bayes (NB) algorithm can get performance results with a weight of 94.40% and an-AUC number of 94.50%, then the PSO-based Support Vectore Machine (SVM) Algorithm with a performance result of 88.20 and an AUC value of 91.10%, and Artificial Neural Network-(NN) based on Particle Swarm Optimizatio (PSO) produces an accuracy performance score with a weight of 99.20% and an accuracy value of 98.50%. Support Vector Machine (SVM) with PSO. While the attributes that have an influence to determine the algorithm on the level of accuracy are Practice Questions, Quizzes, Mid-Semester Exams, and Final Exams. it is evident from our previous studies that the PSO-based neural network algorithm does have a very good advantage. based on ANN is a method that has calculations that build several units of interconnected connectivity, the ANN method with predictive accuracy can be an efficient and good tool for forecasting and classification research in the field of education.

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“…In addition, the quality of the inversion results is also related to the optimization method. A variety of optimization algorithms, such as artifcial bee colony (ABC) [34], PSO [35], gray wolf optimization (GWO) [36], GA [37], whale optimization algorithm (WOA) [38], and bat algorithm (BA) [39], applied in the model parameter identifcation have shown good adaptability. Compared with other methods, PSO has the characteristics of simple implementation, wide application, and strong global search capability.…”

Section: Inversion Model Constructionmentioning

confidence: 99%

Thermal Parameters Inversion Method for Concrete Dam Based on Optimal Temperature Measuring Point Selecting

Wang

Zhou

et al. 2022

Mathematical Problems in Engineering

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Concrete thermal parameters in a natural pouring environment are essential inputs for simulating the temperature field of a concrete dam. This paper proposes a two-stage thermal parameters inversion method for a concrete dam based on optimal temperature measuring point selection to improve the accuracy of parameters. Firstly, a selection method of optimal measuring point for thermal parameters inversion is presented and the temperature response sensitivity of measuring points when the parameters disturb is taken as the critical evaluation index. And then, an inversion model is established based on support vector regression (SVR) and particle swarm optimization (PSO). Finally, the proposed method is applied to the thermal parameter inversion of a concrete dam. The results show that the proposed method is effective for improving the inversion accuracy and obtaining accurate parameters. The average error of the inversion results based on the SVR-PSO model is 28.54% lower than that of the genetic algorithm optimization using a back propagation neural network (BPNN-GA). Besides that, the average error of the inversion results based on the optimal measurement points is 35.57% lower than that of the nonoptimized ones.

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Particle Swarm Optimization Algorithm in Numerical Simulation of Saturated Rock Slope Slip

Cited by 6 publications

References 16 publications

Partial Leader Optimizer

Partial Leader Optimizer

Komparasi Machine Learning Berbasis Pso Untuk Prediksi Tingkat Keberhasilan Belajar Berbasis E-Learning

Thermal Parameters Inversion Method for Concrete Dam Based on Optimal Temperature Measuring Point Selecting

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