Establishment of an Improved Elman Neural Network Model for Predicting the Corrosion Rate of 3C Steel in Marine Environment and Analysis of the Factors Affecting Model Accuracy by Wenbo Jin, Zhuo Chen, Wanying Liu, Qing Quan, Zongxiao Ren

“…Based on the experimental data of corrosion rates of 3C steel in different seawater environments, an improved Elman neural network model was established by using the whale optimization algorithm. …”
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Fault Diagnosis of Power Equipment Based on Improved SVM Algorithm by Youle Song, Yuting Duan, Tong Rao

“…Therefore, this study proposes an improved support vector machine model, combined with grey wolf optimization algorithm, aimed at improving the accuracy and efficiency of power equipment fault diagnosis. …”
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Improved Gradient-Based Optimizer for Modelling Thermal and Hydropower Plants by Ahmed M. Agwa, Yehya I. Mesalam, Mohamed H. Hassan, Mahmoud A. El-Dabah, Anas M. El-Sherif, Salah Kamel

“…In this research, a modified optimization algorithm called an improved gradient-based optimizer (IGBO) is deployed for the optimal extraction of TPP and HPP input-output parameters. …”
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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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Optimization Strategies for Atari Game Environments: Integrating Snake Optimization Algorithm and Energy Valley Optimization in Reinforcement Learning Models by Sadeq Mohammed Kadhm Sarkhi, Hakan Koyuncu

“…One of the biggest problems in gaming AI is related to how we can optimize and adapt a deep reinforcement learning (DRL) model, especially when it is running inside complex, dynamic environments like “PacMan”. …”
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Extraction of the Optimal Parameters of Single-Diode Photovoltaic Cells Using the Earthworm Optimization Algorithm by Fatima Wardi, Mohamed Louzazni, Mohamed Hanine

“…This study introduces a novel method for assessing and deriving the electrical properties of simple diode model solar cells through the utilization of the Earthworm Optimization Algorithm (EOA). …”
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Application of Optimization Algorithms in Voter Service Module Allocation by Edgar Jardón, Marcelo Romero, José-Raymundo Marcial-Romero

“…Allocation models are essential tools for optimally distributing client requests across multiple services under defined restrictions and objective functions. …”
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Improving frequency stability in grid-forming inverters with adaptive model predictive control and novel COA-jDE optimized reinforcement learning by Muhammad Zubair Yameen, Zhigang Lu, Fayez F. M. El-Sousy, Waqar Younis, Baqar Ali Zardari, Abdul Khalique Junejo

“…The offline phase employs a novel Hybrid Crayfish Optimization and Self-Adaptive Differential Evolution Algorithm (COA-jDE) to minimize the cost function $$U_{offline}$$ , deriving optimal control parameters (Q, R) before real-time deployment. …”
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Cooperative Detection-Oriented Formation Design and Optimization of USV Swarms via an Improved Genetic Algorithm by Rui Liang, Dingzhao Li, Haixin Sun, Liangpo Hong

“…We propose a multi-objective formation optimization framework based on an improved genetic algorithm that simultaneously considers the detection coverage area, forward detection width, inter-agent communication, and static obstacle avoidance. …”
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Leveraging Radiomics and Genetic Algorithms to Improve Lung Infection Diagnosis in X-Ray Images Using Machine Learning by A. Beena Godbin, S. Graceline Jasmine

“…To optimize the performance of machine learning models, the paper incorporates genetic algorithms for hyperparameter optimization. …”
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Small Target Detection Algorithm for UAV Aerial Images Based on Improved YOLOv7-tiny by ZHANG Guanghua, LI Congfa, LI Gangying, LU Weidang

“…Future research will focus on further improving the detection of very small targets and optimizing the model for lightweight applications.…”
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Dynamic performance improvement of oscillating water column wave energy conversion system using optimal walrus optimization algorithm-based control strategy by Habiba A. ElDemery, Hany M. Hasanien, Mohammed Alharbi, Chuanyu Sun, Dina A. Zaky

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Improved satellite resource allocation algorithm based on DRL and MOP by Pei ZHANG, Shuaijun LIU, Zhiguo MA, Xiaohui WANG, Junde SONG

“…In view of the multi-objective optimization (MOP) problem of sequential decision-making for resource allocations in multi-beam satellite systems,a deep reinforcement learning(DRL) based DRL-MOP algorithm was proposed to improve the system performance and user satisfaction degree.With considering the normalized weighted sum of spectrum efficiency,energy efficiency,and satisfaction index as the optimization goal,the dynamically changing system environments and user arrival model were built by the proposed algorithm,and the optimization of the accumulative performance in satellite systems based on DRL and MOP was realized.Simulation results show that the proposed algorithm can solve the MOP problem with rapid convergence ability and low complexity,and it is obviously superior to other algorithms in terms of system performance and user satisfaction optimization.…”
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Intelligent recommendation algorithm for social networks based on improving a generalized regression neural network by Gongcai Wu

“…In this study, the social network model was used for modeling, and the recommendation model was improved based on variational modal decomposition and the whale optimization algorithm. …”
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A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization Algorithms by Mohammad Ghattas, Antonio M. Mora, Suhail Odeh

“…Similarly, Random Forest models showed a slight improvement, reaching 81% with feature selection versus 80% without. …”
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Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

“…Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.…”
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Prediction method of soil water content based on SVM optimized by improved salp swarm algorithm by Xiaoqiang ZHAO, Fan YANG, Zhufeng YAN

“…Aiming at the problems of low accuracy and low efficiency of traditional soil water content prediction methods, support vector machine (SVM) was used to establish a prediction model, and the soil water content prediction method based on SVM optimized was proposed by the improved salp swarm algorithm.Firstly, the opposition-based learning and chaotic optimization were introduced to improve the standard salp swarm algorithm to solve the problem that the algorithm was easy to fall into the local optimal solution and its convergence speed was slow.Secondly, the improved salp swarm algorithm was used to optimize the parameters that affect the performance of SVM and the corresponding prediction model was built.Finally, the proposed model was compared with the particle swarm optimization SVM and the whale algorithm optimized SVM prediction model.The experimental results show that the mean square error and decision coefficient of the proposed model are 0.42 and 0.901, which are better than the other two models which verified the effectiveness of the proposed method.…”
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An Adaptive Layering Dual-Parameter Regularization Inversion Method for an Improved Giant Trevally Optimizer Algorithm by Chao Tan, Menghao Sun, Wei Liu, Wenrui Tan, Xiaoling Zhang, Chengang Zhu, Da Li

“…Subsequently, the current model parameters of the inversion objective function are optimized using the Giant Trevally Optimizer (GTO) algorithm, improved by the Particle Swarm Optimization (PSO) algorithm. …”
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Optimization method for cloud manufacturing service composition based on the improved artificial bee colony algorithm by Qiang HU, Yuqing TIAN, Haoquan QI, Peng WU, Qingxue LIU

“…To improve the optimization quality, efficiency and stability of cloud manufacturing service composition, a optimization method for cloud manufacturing service composition based on improved artificial bee colony algorithm was proposed.Firstly, three methods of service collaboration quality calculation under cloud manufacturing service composition scenario were put forward.Then, the optimization model with service collaboration quality was constructed.Finally, an artificial bee colony algorithm with multi-search strategy island model was designed to solve the optimal cloud manufacturing service composition.The experimental results show that the proposed algorithm is superior to the current popular improved artificial bee colony algorithms and other swarm intelligence algorithms in terms of optimization quality, efficiency and stability.…”
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FAULT DIAGNOSIS OF SCRAPER CONVEYOR REDUCER BASED ON IMPROVED FIREFLY ALGORITHM TO OPTIMIZE NEURAL NETWORK by MAO Jun, GUO Hao, CHEN HongYue

“…The second application feature data sample for fault diagnosis model based on neural network training. Using the improved firefly algorithm to optimize neural network weights and threshold, to speed up the optimum value of, get the optimal model of the network. …”
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