Adaptive component crossover for differential evolution in solving single-objective optimization problems by Sopov Anton, Sopov Evgenii

“…One of the most popular and promising evolutionary methods is the Differential Evolution algorithms. …”
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Optimizing XGBoost Hyperparameters for Credit Scoring Classification Using Weighted Cognitive Avoidance Particle Swarm by Atul Vikas Lakra, Sudarson Jena, Kaushik Mishra

“…The proposed WCAPSO-XGB model tunes the hyperparameters of XGBoost and classifies the credit scoring, and the experimental results are compared with various classifier such as Random Forest (RF), K-neighbors (KNN), Gaussian Naive Bayes (NB), AdaBoost, Gradient Boosting, Logistic Regression (LR), Neural Network (NN), Decision Tree (DT) and Linear Discriminant Analysis (LDA), and hyperparameter optimization methods, such as Grid Search (GS), Random Search (RS), Bays Optimization, Optuna Optimization, Hybrid Snake Optimizer Algorithm (HSOA), Exploratory Cuckoo Search, island Cuckoo Search (iCSPM and iCSPM2), and Improved SSA (ISSA) with HDPM, on four different datasets with a varying number of instances from small to large. …”
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Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information by Fu Yan, Jianzhong Xu, Kumchol Yun

“…The comparison results for the 23 benchmark functions show that the proposed DGWO algorithm performs significantly better than the GWO and its improved variant for most benchmarks. …”
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Color Dominance-Based Polynomial Optimization Segmentation for Identifying Tomato Leaves and Fruits by Juan Pablo Guerra Ibarra, Francisco Javier Cuevas de la Rosa, Alicia Linares Ramirez

“…Similarly, a UNetmodel is used for semantic segmentation, the results of which are inferior to those obtained by the proposed interpolation optimization method. The most significant contribution of the interpolation method is that it requires only a single iteration to generate the initial data, in contrast to the iterative search required by the greedy algorithm and the lengthy training process and video card dependency of the UNet model. …”
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Safety Helmet Wearing Detection Based on Jetson Nano and Improved YOLOv5 by Zaihui Deng, Chong Yao, Qiyu Yin

“…Finally, the YOLOv5-SN network is obtained by improving the YOLOv5 model, and the optimized model is deployed on Jetson Nano for testing. …”
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A Linear Regression Prediction-Based Dynamic Multi-Objective Evolutionary Algorithm with Correlations of Pareto Front Points by Junxia Ma, Yongxuan Sang, Yaoli Xu, Bo Wang

“…Specifically, when the DMOP environment changes, this paper first constructs a spatio-temporal correlation model between various key points of the PF based on the linear regression algorithm; then, based on the constructed model, predicts a new location for each key point in the new environment; subsequently, constructs a sub-population by introducing the Gaussian noise into the predicted location to improve the generalization ability; and then, utilizes the idea of NSGA-II-B to construct another sub-population to further improve the population diversity; finally, combining the previous two sub-populations, re-initializing a new population to adapt to the new environment through a random replacement strategy. …”
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Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies by Diego Calderon, Mohammad Najafi

“…Optimal placement is achieved with a k-Nearest Neighbors (kNN) model tuned with minimal topological and physical pipeline system features. …”
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An optimized machine learning framework for predicting and interpreting corporate ESG greenwashing behavior. by Fanlong Zeng, Jintao Wang, Chaoyan Zeng

“…The framework integrates an Improved Hunter-Prey Optimization (IHPO) algorithm, an eXtreme Gradient Boosting (XGBoost) model, and SHapley Additive exPlanations (SHAP) theory to predict and interpret corporate ESG greenwashing behavior. …”
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Multi-objective operation optimization method of microgrid considering the influence of electric vehicle by Tiefeng Xu, Xiaofang Meng, Fangfang Zheng, Yiduo Zhang, Xin Wu, Mingyang Li

“…Taking the minimum total operating cost and the minimum peak-valley difference of the microgrid in one day as the optimization objective, and considering many constraints such as power balance constraints and output constraints of distributed generation units, the multi-objective optimization function is transformed into a single-objective optimization function by linear weighting method, and the model is solved by particle swarm optimization algorithm. …”
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An ensemble of deep representation learning with metaheuristic optimisation algorithm for critical health monitoring using internet of medical things by Mai Alduailij

“…To further optimize model performance, the pelican optimization algorithm (POA) is utilized for hyperparameter tuning to ensure that the optimum hyperparameters are chosen for enhanced accuracy. …”
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Towards few-shot learning with triplet metric learning and Kullback-Leibler optimization by Yukun Liu, Xiaojing Wei, Daming Shi, Dan Xiang, Junliu Zhong, Hai Su

“…In training, the deep learning and expectation-maximization algorithm are used to optimize models. Intensive experiments have been conducted on three popular benchmark datasets, and the experimental results show that this method significantly improves the classification ability of few-shot learning tasks and obtains the most advanced performance.…”
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Optimal scheduling of BIES with multi-energy flow coupling based on deep RL by XIA Xuhua, YANG Jiandi, SHI Yongtao

“…Secondly， the state space， action space， and reward function for the operational dispatch strategy are designed using deep RL， and a low-carbon economic and optimal dispatch framework is constructed using the ​soft actor-critic （SAC） algorithm.​ …”
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Dynamic balance optimization method for aero-engine rotor without trial weight by Lingli Jiang, Changzhi Shi, Xuejun Li, Hui Ma, Yiming Cao

“…Subsequently, equilibrium equations are established to relate the unbalanced response to rotor amplitude. A multi-strategy improved sparrow search algorithm is then applied. …”
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Hybrid Gradient Descent Grey Wolf Optimizer for Machine Learning Performance Enhancement by Sri Rossa Aisyah Puteri Baharie, Sugiyarto Surono, Aris Thobirin

“…Advancements in machine learning have enabled the development of more accurate and efficient health prediction models. This study aims to improve diabetes prediction performance using the Support Vector Machine (SVM) model optimized with the Hybrid Gradient Descent Gray Wolf Optimizer (HGD-GWO) method. …”
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APG mergence and topological potential optimization based heuristic user association strategy by Zhirui HU, Meihua BI, Fangmin XU, Meilin HE, Changliang ZHENG

“…Methods:The network scalable degree was designed as a measure of scalability,and then a user association strategy to improve network scalable degree was studied by using optimization theory. 1) For modelling the optimization problem, firstly, the network coupling degree, representing the degree of association among nodes, was constructed to establish the mathematical relationship between the network scalable degree and AP group (APG).Thus,the problem of improving the network scalable degree was modeled as the problem of minimizing the network coupling degree.Then,a multi-objective optimization problem of minimum network coupling degree and maximum user rate was established to find the balance between network scalable degree and network service quality. 2) For solving the optimization problem,to avoid the high computational complexity,a heuristic user association strategy based on APG mergence and topological potential optimization was proposed.With the proposed algorithm,the number of APG could be reduced by APG mergence,and the number of APG that AP belongs to could be reduced by AP exiting APG. …”
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Deep Reinforcement Learning-Based Motion Control Optimization for Defect Detection System by Yuhuan Cai, Liye Zhao, Xingyu Chen, Zhenjun Li

“…To address these challenges, this study proposes a deep reinforcement learning-based control scheme, leveraging DRL’s capabilities to optimize system performance. Specifically, the TD3 algorithm, featuring a dual-critic structure, is employed to enhance control precision within predefined state and action spaces. …”
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Artificial intelligence for smart irrigation: Reducing water consumption and improving agricultural output by Arlanova A. A., Hojamkuliyeva B. A., Babanazarov N. Sh., Arlanov M. S.

“…Existing approaches to using artificial intelligence in agricultural technologies for predicting water needs and regulating irrigation are examined. A mathematical model based on machine learning algorithms is developed to predict the optimal water volume required for irrigation of agricultural crops. …”
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Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network by Li-li Li, Kun Chen, Jian-min Gao, Hui Li

“…In order to eliminate the defect of experience value, the key parameter of PNN was optimized by the improved (SGA) single-target optimization genetic algorithm, which made PNN achieve a higher rate of recognition accuracy than PNN optimized by standard genetic algorithm. …”
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Study on the Evolutionary Characteristics of Spatial and Temporal Patterns and Decoupling Effect of Urban Carbon Emissions in the Yangtze River Delta Region Based on Neural Network... by Xichun Luo, Chaoming Cai, Honghao Zhao

“…To improve the performance of neural network models, the Aquila Optimizer (AO) algorithm is introduced to optimize the hyper-parameter values in the back-propagation (BP) neural network model in this research due to the appealing searching capability of AO over traditional algorithms. …”
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Solving the Traveling Salesman Problem Based on The Genetic Reactive Bone Route Algorithm whit Ant Colony System by Majid Yousefikhoshbakht, Nasrin Malekzadeh, Mohammad Sedighpour

“…The TSP is considered one of the most well-known combinatorial optimization tasks and researchers have paid so much attention to the TSP for many years. …”
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