Article
Green & Sustainable Science & Technology
Rana Muhammad Adnan Ikram, Imran Khan, Hossein Moayedi, Atefeh Ahmadi Dehrashid, Ismail Elkhrachy, Binh Nguyen Le
Summary: Landslide susceptibility is an essential tool for managing and planning human settlements in order to mitigate/prevent the risks of landslides. In this paper, the SCE and VSA algorithms were used to optimize the artificial neural network (ANN) model for generating landslide susceptibility maps in the Kurdistan province of Iran. The results showed that the VSA-ANN model had better predictive capability and optimization of the ANN model structure and computational parameters compared to the SCE-ANN model.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2023)
Article
Computer Science, Artificial Intelligence
Kambiz Gholami, Hassan Olfat, Jafar Gholami
Summary: The study proposed a new hybrid algorithm HJCSA by combining JAYA algorithm with CSA algorithm to enhance performance, through evaluation on 20 benchmark functions and comparison with other algorithms, it is found that the optimization algorithm has advantages in better convergence and discovering more accurate solutions.
Article
Computer Science, Information Systems
Le Yan, Jianjun Chen, Qi Li, Jiafa Mao, Weiguo Sheng
Summary: Preserving an appropriate population diversity is critical for the performance of evolutionary algorithms. In this paper, a Co-evolutionary niching strategy (CoEN) is proposed to dynamically evolve appropriate niching methods and incorporate them into differential evolution (DE) to maintain population diversity. Extensive testing on benchmark functions from CEC2019 and CEC2014 demonstrates the significance of the proposed CoEN, showing that incorporating CoEN allows the resulting DE to achieve better or competitive performance compared to related algorithms.
Article
Computer Science, Information Systems
Zheng-Ming Gao, Juan Zhao, Yu-Rong Hu, Hua-Feng Chen
Summary: With the introduction of symmetry or non-symmetry as a new characteristic affecting the capability of algorithms in optimization, experimental results showed that most of the non-symmetric benchmark functions were difficult to optimize. None of the algorithms involved could optimize all functions, indicating the need for new methods and improvements for nature-inspired algorithms.
Article
Computer Science, Artificial Intelligence
M. T. Indu, C. Shunmuga Velayutham
Summary: This paper proposes an automated framework called MeSCEDE for designing high-level multi-population ensemble Differential Evolution algorithms. By using Grammatical Evolution as the meta-evolutionary algorithm, MeSCEDE is able to evolve effective ensemble designs and demonstrates competitive performance on benchmark functions and real-world problems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
El-Sayed M. El-Kenawy, Seyedali Mirjalili, Fawaz Alassery, Yu-Dong Zhang, Marwa Metwally Eid, Shady Y. El-Mashad, Bandar Abdullah Aloyaydi, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid
Summary: This paper introduces a Sine Cosine hybrid optimization algorithm with Modified Whale Optimization Algorithm (SCMWOA), which aims to solve problems with continuous and binary decision variables. Through testing on various datasets and benchmark functions, the results demonstrate the superior performance of the algorithm in feature selection and engineering design.
Article
Multidisciplinary Sciences
Baojun Fu, Jianpei Zhang, Wenjing Li, Meijing Zhang, Yu He, Qiujin Mao
Summary: An improved differential evolution algorithm based on network discretization is proposed to solve the influence maximization problem, and experimental results show that the algorithm outperforms comparison algorithms.
Article
Computer Science, Interdisciplinary Applications
S. N. Poojitha, V Jothiprakash
Summary: This study introduces a novel hybrid model combining evolutionary and swarm intelligence techniques, referred to as DE-KHA, for optimizing water distribution networks. Experimental results demonstrate that DE-KHA outperforms other competing algorithms in improving search performance and computational efficiency.
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
(2022)
Article
Mathematics
Akram Belazi, Hector Migallon, Daniel Gonzalez-Sanchez, Jorge Gonzalez-Garcia, Antonio Jimeno-Morenilla, Jose-Luis Sanchez-Romero
Summary: This paper introduces an enhanced version of the sine cosine algorithm (ESCA algorithm) and designs several parallel algorithms to improve solution accuracy and convergence speed. Experimental results demonstrate the superiority of the proposed algorithm and its outstanding performance in engineering design problems. Additionally, the overall performance of the algorithm is statistically validated using non-parametric statistical tests.
Article
Chemistry, Multidisciplinary
Xin Zhao, Zhili Tang, Fan Cao, Caicheng Zhu, Jacques Periaux
Summary: This paper proposes an efficient hybrid evolutionary optimization method, called HCGA, which combines cultural algorithm (CA) with genetic algorithm (GA) to solve complex engineering optimization problems. By reconstructing the cultural framework, using a knowledge-guided t-mutation operator, and balancing exploration and exploitation, HCGA effectively avoids local optima and improves optimization efficiency. Numerical experiments and comparisons show that HCGA outperforms other algorithms in terms of comprehensive performance, especially for high-dimensional problems. Its application to aerodynamic optimization design demonstrates its potential in practical engineering applications.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Chuan Wang, Minyi Xu, Qinjin Zhang, Ruizheng Jiang, Jinhong Feng, Yi Wei, Yancheng Liu
Summary: This study introduces a new cooperative co-evolution algorithm to identify parameters of lithium-ion batteries, avoiding linearization or pre-assumption, and demonstrates its effectiveness through comprehensive experimental results.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Sanjoy Debnath, Srimanta Baishya, Debarati Sen, Wasim Arif
Summary: The DADE algorithm combines the advantages of the dragonfly algorithm and differential evolution algorithm, addressing the lack of internal memory and solution diversity issues in the dragonfly algorithm. By storing the best solution in memory and proceeding with DE, it enhances population diversity and accelerates the search for global optima.
ENGINEERING WITH COMPUTERS
(2021)
Article
Computer Science, Information Systems
Rasmita Dash, Rajashree Dash, Rasmita Rautray
Summary: Microarray technology has been widely used in biomedical research, but its efficient application in this field remains challenging and expensive. This study proposes a new metaheuristic approach, utilizing binary shuffled frog leaping algorithm for gene selection, and demonstrates the superiority of the selected gene subset through comparison with various classifiers.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Yue-Jiao Gong, Yi-Wen Liu, Ying Lin, Wei-Neng Chen, Jun Zhang
Summary: This paper presents a two-stage taxi-passenger matching system that optimizes the quality and profit of taxi-passenger matching by utilizing a fuzzy controller and a polynomial Kuhn-Munkres algorithm.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Yiping Xiong, Shuyin Xia, Caoxiao Li, Xiaoyu Lian, Bin Hou, Guoyin Wang
Summary: This study proposes a multi-granularity framework that utilizes completely random trees and computes the spatial distribution of individuals to divide the feasible region into different granularities. Through sampling and migration strategy, it takes full advantage of the historical information to improve the convergence speed and optimization accuracy of the algorithm.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Mathematics, Interdisciplinary Applications
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Gabriel Trierweiler Ribeiro, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: Efficient models for short-term load forecasting in electricity distribution and generation systems are crucial for companies' energetic planning. In this study, an ensemble learning model based on dual decomposition approach, machine learning models and hyperparameters optimization is proposed. The model successfully decomposes the time series and handles the non-linearities, and achieves accurate load forecasting results with reduced errors.
CHAOS SOLITONS & FRACTALS
(2023)
Article
Energy & Fuels
Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: The cost of electricity and gas has a direct impact on people's everyday routines, but the value of electricity is closely related to spot market prices, which can increase in winter due to higher energy demand. Existing models for forecasting energy costs are not robust enough due to competition, seasonal changes, and other variables. This study proposes combining seasonal and trend decomposition using LOESS and Facebook Prophet methodologies to improve the accuracy of analyzing time series data on Italian electricity spot prices.
Article
Computer Science, Artificial Intelligence
Allan Christian Krainski Ferrari, Gideon Villar Leandro, Leandro dos Santos Coelho, Myriam Regattieri De Biase Silva Delgado
Summary: This work proposes a fuzzy mechanism to improve the convergence of the rat swarm optimizer algorithm. The proposed fuzzy model uses the normalized fitness and population diversity as input. The results show that the fuzzy mechanism improves convergence and is competitive with other metaheuristics.
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
(2023)
Article
Computer Science, Information Systems
Andre Armstrong Janino Cizotto, Rodrigo Clemente Thom de Souza, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: The objective of this study is to validate the use of deep neural networks (DNNs) for segmenting and classifying web elements. A dataset of 2200 images representing 10 distinct classes was created using screenshots of real web pages. The study contributes by validating classification-only convolutional neural networks (CNNs) with the support of Class Activation Mapping (CAM), a weakly-supervised semantic segmentation technique. The best-performing model achieved a final accuracy rating of 95.71%, but improvements are still needed on the dataset and architecture for real-time dynamic web page building.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Chemistry, Analytical
Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: Insulators installed outdoors are prone to accumulation of contaminants, causing increased conductivity and leakage current, eventually leading to flashover. To enhance power system reliability, it is possible to predict fault development and potential shutdown by evaluating the increase in leakage current. This paper proposes a method, optimized EWT-Seq2Seq-LSTM with attention, which combines empirical wavelet transform (EWT) to reduce non-representative variations and the attention mechanism with LSTM recurrent network for prediction. The model achieved a 10.17% lower mean square error (MSE) compared to standard LSTM and a 5.36% lower MSE compared to the model without optimization, demonstrating the effectiveness of the attention mechanism and hyperparameter optimization.
Article
Chemistry, Analytical
Andressa Borre, Laio Oriel Seman, Eduardo Camponogara, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: In this paper, the issue of predicting electrical machine failures by predicting possible anomalies in the data is addressed through time series analysis. The dataset is used to train a hybrid CNN-LSTM architecture, which employs quantile regression to manage uncertainties in the data. The results show that this approach outperforms traditional reference models, making it beneficial for companies to optimize maintenance schedules and improve the performance of their electric machines.
Article
Chemistry, Analytical
Guilherme Augusto Silva Surek, Laio Oriel Seman, Stefano Frizzo Stefenon, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper aims to evaluate and map the current scenario of human actions in red, green, and blue videos using deep learning models. A semi-supervised learning approach is employed to evaluate a residual network (ResNet) and a vision transformer architecture (ViT). The results obtained using a bi-dimensional ViT structure demonstrated great performance in human action recognition, achieving an accuracy of 96.7% on the HMDB51 dataset.
Article
Chemistry, Analytical
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Jose Henrique Kleinubing Larcher, Andre Mendes, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper proposes a new hybrid framework combining STACK ensemble learning and a JADE algorithm for nonlinear system identification. The model performs well in decoding EEG signals, achieving an average explanation of 94.50% and 67.50% of data variability, and outperforms other methods in terms of accuracy.
Article
Chemistry, Analytical
Stefano Frizzo Stefenon, Laio Oriel Seman, Nemesio Fava Sopelsa Neto, Luiz Henrique Meyer, Viviana Cocco Mariani, Leandro dos Santos Coelho
Summary: This paper presents a novel hybrid method for fault prediction based on the time series of leakage current of contaminated insulators. The proposed CFRW-GMDH method, with a root-mean-squared error of 3.44x10(-12), outperformed other models in fault prediction. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply.
Article
Mathematics, Applied
Peter Frolkovic, Nikola Gajdosova
Summary: This paper presents compact semi-implicit finite difference schemes for solving advection problems using level set methods. Through numerical tests and stability analysis, the accuracy and stability of the proposed schemes are verified.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Md. Rajib Arefin, Jun Tanimoto
Summary: Human behaviors are strongly influenced by social norms, and this study shows that injunctive social norms can lead to bi-stability in evolutionary games. Different games exhibit different outcomes, with some showing the possibility of coexistence or a stable equilibrium.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Dingyi Du, Chunhong Fu, Qingxiang Xu
Summary: A correction and improvement are made on a recent joint work by the second and third authors. An optimal perturbation bound is also clarified for certain 2 x 2 Hermitian matrices.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Pingrui Zhang, Xiaoyun Jiang, Junqing Jia
Summary: In this study, improved uniform error bounds are developed for the long-time dynamics of the nonlinear space fractional Dirac equation in two dimensions. The equation is discretized in time using the Strang splitting method and in space using the Fourier pseudospectral method. The major local truncation error of the numerical methods is established, and improved uniform error estimates are rigorously demonstrated for the semi-discrete scheme and full-discretization. Numerical investigations are presented to verify the error bounds and illustrate the long-time dynamical behaviors of the equation with honeycomb lattice potentials.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Kuan Zou, Wenchen Han, Lan Zhang, Changwei Huang
Summary: This research extends the spatial PGG on hypergraphs and allows cooperators to allocate investments unevenly. The results show that allocating more resources to profitable groups can effectively promote cooperation. Additionally, a moderate negative value of investment preference leads to the lowest level of cooperation.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Kui Du
Summary: This article introduces two new regularized randomized iterative algorithms for finding solutions with certain structures of a linear system ABx = b. Compared to other randomized iterative algorithms, these new algorithms can find sparse solutions and have better performance.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Shadi Malek Bagomghaleh, Saeed Pishbin, Gholamhossein Gholami
Summary: This study combines the concept of vanishing delay arguments with a linear system of integral-algebraic equations (IAEs) for the first time. The piecewise collocation scheme is used to numerically solve the Hessenberg type IAEs system with vanishing delays. Well-established results regarding regularity, existence, uniqueness, and convergence of the solution are presented. Two test problems are studied to verify the theoretical achievements in practice.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Qi Hu, Tao Jin, Yulian Jiang, Xingwen Liu
Summary: Public supervision plays an important role in guiding and influencing individual behavior. This study proposes a reputation incentives mechanism with public supervision, where each player has the authority to evaluate others. Numerical simulations show that reputation provides positive incentives for cooperation.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Werner M. Seiler, Matthias Seiss
Summary: This article proposes a geometric approach for the numerical integration of (systems of) quasi-linear differential equations with singular initial and boundary value problems. It transforms the original problem into computing the unstable manifold at a stationary point of an associated vector field, allowing efficient and robust solutions. Additionally, the shooting method is employed for boundary value problems. Examples of (generalized) Lane-Emden equations and the Thomas-Fermi equation are discussed.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Lisandro A. Raviola, Mariano F. De Leo
Summary: We evaluated the performance of novel numerical methods for solving one-dimensional nonlinear fractional dispersive and dissipative evolution equations and showed that the proposed methods are effective in terms of accuracy and computational cost. They can be applied to both irreversible models and dissipative solitons, offering a promising alternative for solving a wide range of evolutionary partial differential equations.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Yong Wang, Jie Zhong, Qinyao Pan, Ning Li
Summary: This paper studies the set stability of Boolean networks using the semi-tensor product of matrices. It introduces an index-vector and an algorithm to verify and achieve set stability, and proposes a hybrid pinning control technique to reduce computational complexity. The issue of synchronization is also discussed, and simulations are presented to demonstrate the effectiveness of the results obtained.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Ling Cheng, Sirui Zhang, Yingchun Wang
Summary: This paper considers the optimal capacity allocation problem of integrated energy systems (IESs) with power-gas systems for clean energy consumption. It establishes power-gas network models with equality and inequality constraints, and designs a novel full distributed cooperative optimal regulation scheme to tackle this problem. A distributed projection operator is developed to handle the inequality constraints in IESs. The simulation demonstrates the effectiveness of the distributed optimization approach.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Abdurrahim Toktas, Ugur Erkan, Suo Gao, Chanil Pak
Summary: This study proposes a novel image encryption scheme based on the Bessel map, which ensures the security and randomness of the ciphered images through the chaotic characteristics and complexity of the Bessel map.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Xinjie Fu, Jinrong Wang
Summary: In this paper, we establish an SAIQR epidemic network model and explore the global stability of the disease in both disease-free and endemic equilibria. We also consider the control of epidemic transmission through non-instantaneous impulsive vaccination and demonstrate the sustainability of the model. Finally, we validate the results through numerical simulations using a scale-free network.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
Article
Mathematics, Applied
Maria Han Veiga, Lorenzo Micalizzi, Davide Torlo
Summary: The paper focuses on the iterative discretization of weak formulations in the context of ODE problems. Several strategies to improve the accuracy of the method are proposed, and the method is combined with a Deferred Correction framework to introduce efficient p-adaptive modifications. Analytical and numerical results demonstrate the stability and computational efficiency of the modified methods.
APPLIED MATHEMATICS AND COMPUTATION
(2024)
