Detection of Plants Leaf Diseases using Swarm Optimization Algorithms by Saud Abdul Razzaq, Baydaa Khaleel

“…In this paper, a new method is proposed to classify and distinguish a group of eight different plants to healthy and unhealthy based on the leaf images of these plants They are apples, cherries, grapes, peaches, peppers, potatoes, strawberries, and tomatoes using a hybrid optimization algorithm. In the first stage, the plant leaf images were collected and pre-processed to remove noise and improve contrast. …”
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A Review of Stochastic Optimization Algorithms Applied in Food Engineering by Laís Koop, Nadia Maria do Valle Ramos, Adrián Bonilla-Petriciolet, Marcos Lúcio Corazza, Fernando Augusto Pedersen Voll

“…It was observed that evolutionary methods are the most applied in solving food engineering optimization problems where the genetic algorithm and differential evolution stand out. …”
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MODELLING FLUCTUATIONS OF GROUNDWATER LEVEL USING MACHINE LEARNING ALGORITHMS IN THE SOKOTO BASIN by Samson Alfa, Haruna Garba, Augustine Odeh

“…Hyperparameters for the XGBoost model were fine-tuned using grid search techniques, resulting in optimal settings that significantly enhanced predictive accuracy with Mean Absolute Error (MAE) ranging from 0.016 – 0.757m and Root Mean Square Error (RMSE) ranging from 0.051 - 2.859m. …”
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Simulated Annealing-Based Krill Herd Algorithm for Global Optimization by Gai-Ge Wang, Lihong Guo, Amir Hossein Gandomi, Amir Hossein Alavi, Hong Duan

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Simple gravitational particle swarm algorithm for multimodal optimization problems. by Yoshikazu Yamanaka, Katsutoshi Yoshida

“…Aiming to help the decision makers even if they are non-experts in optimization algorithms, this study proposes a new and simple multimodal optimization (MMO) algorithm called the gravitational particle swarm algorithm (GPSA). …”
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Residual Life Prediction of Proton Exchange Membrane Fuel Cell Based on Improved ESN by Tiejiang YUAN, Rongsheng LI, Jiandong KANG, Huaguang YAN

“…Aiming at the problem that the current residual effective life prediction (RUL) technique for proton exchange membrane fuel cells (PEMFCs) has poor prediction effect in the medium and long term, a residual life prediction method based on the Improved Gray Wolf Optimization algorithm (IGWO) and Echo State Network (ESN) is proposed, in which the voltage of the electric stack is firstly selected as a health indicator, and the PEMFC dataset is processed by using convolutional smoothing filtering method to carry out data Smoothing and normalization are used to effectively reduce the interference of outliers on the subsequent model training. …”
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Optimal geometrical selection of skin mesh: experimental analysis and numerical optimization by Mehdi Khayami, Aisa Rassoli, Alireza Feizkhah

“…Hyperelastic properties of healthy and meshed skin were obtained through uniaxial tensile tests, and different geometries were analyzed using Abaqus. The optimal mesh geometry was then determined using genetic algorithms in Abaqus and MATLAB. …”
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Application of Swarm Intelligence Optimization Algorithm in Logistics Delivery Path Optimization under the Background of Big Data by Guofu Zhao

“…The hybrid algorithm can effectively improve the optimization efficiency of VRPTW, lay a foundation for solving large-scale VRPTW, and provide new research ideas and methods. …”
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Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms by Nian Liu, Yuehan Zhao

“…Methodology In order to reduce line losses, a loss optimization model for low and medium voltage distribution networks based on an improved Gray Wolf optimization support vector machine is proposed. …”
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Forecasting Influenza Trends Using Decomposition Technique and LightGBM Optimized by Grey Wolf Optimizer Algorithm by Yonghui Duan, Chen Li, Xiang Wang, Yibin Guo, Hao Wang

“…Accurate influenza prediction is a critical issue in public health and serves as an essential tool for epidemiological studies. This paper seeks to improve the prediction accuracy of influenza-like illness (ILI) proportions by proposing a novel predictive model that integrates a data decomposition technique with the Grey Wolf Optimizer (GWO) algorithm, aiming to overcome the limitations of current prediction methods. …”
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Comprehensive Study of Nonlinear Maglev System Utilizing COOT Optimized FOPID Controller by Marabathina Maheedhar, T. Deepa

“…To improve the performance of the magnetic levitation system, the most recent metaheuristic COOT algorithm was first employed in this study to tune the Fractional Order Proportional Integral and Derivative (FOPID) controller. …”
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Optimizing Ontology Alignment through Improved NSGA-II by Yikun Huang, Xingsi Xue, Chao Jiang

“…Over the past decades, a large number of complex optimization problems have been widely addressed through multiobjective evolutionary algorithms (MOEAs), and the knee solutions of the Pareto front (PF) are most likely to be fitting for the decision maker (DM) without any user preferences. …”
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An improved electrocardiogram arrhythmia classification performance with feature optimization by Annisa Darmawahyuni, Siti Nurmaini, Bambang Tutuko, Muhammad Naufal Rachmatullah, Firdaus Firdaus, Ade Iriani Sapitri, Anggun Islami, Jordan Marcelino, Rendy Isdwanta, Muhammad Ikhwan Perwira

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Continuous prediction of knee joint angle in lower limbs based on sEMG: a method combining an improved ZOA optimizer and attention-enhanced GRU by Jian Lv, Binhao Huang, Ligang Qiang

“…Experimental evaluations across three motion tasks—level walking, stair ascent, and stair descent—demonstrated that the proposed method achieved a minimum root mean square error (RMSE) of 1.31°, with over 50% reduction in feature dimensionality, significantly outperforming Genetic Algorithm (GA), Zebra Optimization Algorithm (ZOA), Liver Cancer Algorithm (LCA), and Pied Kingfisher Optimizer (PKO). …”
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Novel nonlinear wind power prediction based on improved iterative algorithm by Fu Zhen-yu, Lin Gui-quan, Tian Wei-da, Pan Zhi-hao, Zhang Wei-cong

“…To effectively improve the accuracy of wind power prediction and reduce the load on the power grid, a new nonlinear wind power prediction model based on an improved iterative learning algorithm was investigated. …”
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An Application of Quasi Newton Algorithm and Improvement of Sliding Surface for Robot Control by Chi-Ngon Nguyen, Thanh Tung Pham

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Provide a method to diagnose and optimize diabetes using data mining methods and firefly algorithm by Reza Molaee Fard

“…In this research, the DBSCAN clustering algorithm is used to cluster the data. Then, using SVM, we classify the data to identify useful data, and finally, with the firefly algorithm, we increase the obtained data to increase we optimize performance with this algorithm. …”
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An Improved Shuffled Frog Leaping Algorithm for Electrical Resistivity Tomography Inversion by Fuyu Jiang, Likun Gao, Run Han, Minghui Dai, Haijun Chen, Jiong Ni, Yao Lei, Xiaoyu Xu, Sheng Zhang

“…Second, an adaptive movement operator is constructed to dynamically regulate the step size of the search, enhancing the guiding effect of the optimal solution. In synthetic data tests of three typical electrical models, including a high-resistivity anomaly with 5% random noise, a normal fault, and a reverse fault, the improved algorithm shows an approximately 2.3 times higher accuracy in boundary identification of the anomaly body compared to the least squares (LS) method and standard SFLA. …”
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Impact and integration of electric vehicles on renewable energy based microgrid: Frequency profile improvement by a-SCA optimized FO-Fuzzy PSS approach by Prakash Chandra Sahu

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Control Optimization of Stochastic Systems Based on Adaptive Correction CKF Algorithm by FengJun Hu, Qian Zhang, Gang Wu

“…The control model is established by using the CKF algorithm, the covariance matrix of standard CKF is optimized by square root filter, the adaptive correction of error covariance matrix is realized by adding memory factor to the filter, and the disturbance factors in nonlinear time-varying discrete stochastic systems are eliminated by multistep feedback predictive control strategy, so as to improve the robustness of the algorithm. …”
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