Reliability growth model of quantum direct current electricity meter software based on optimization network by TIAN Teng, QIU Rujia, ZHAO Long, GENG Jiaqi, WANG Enhui, SUN Yu

“…This improves the modeling efficiency by 18 times and significantly improves global optimization ability of the back propagation neural network. …”
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Research on Interval Probability Prediction and Optimization of Vegetation Productivity in Hetao Irrigation District Based on Improved TCLA Model by Jie Ren, Delong Tian, Hexiang Zheng, Guoshuai Wang, Zekun Li

“…Experimental data indicate that the TCLA model improves prediction accuracy by 10.57–26.47% compared to conventional models (Long Short-Term Memory (LSTM), Transformer). …”
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Modeling Analysis and Simulation Verification for Drive Tooth Stress of Rubber Track Wheel by Zihan Zhao, Xihui Mu, Fengpo Du, Jianhua Guo

“…Firstly，based on structure parameters and transmission principle，the drive tooth profile equation is established and determining mapping parameters by the improved Powell algorithm. The optimization results show that the accuracy of the mapping tooth profile obtained by this method is 0.12%，which effectively improves the mapping accuracy. …”
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STRUCTURE RELIABILITY CALCULATION METHOD BASED ON IMPROVED NEURAL NETWORK by LI YongHua, CHEN Peng, TIAN ZongRui, CHEN ZhiHao

“…Aiming at the problems that traditional BP neural network surrogate model had deficiency of fitting accuracy and computational efficiency, the Mind Evolutionary Algorithm was used to optimize BP neural network and an improved BP neural network surrogate model reliability calculation method was proposed. …”
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SAHAANN: A NOVEL EVOLUTIONARY ARTIFICIAL NEURAL NETWORK FOR IMPROVED FINANCIAL TIME SERIES FORECASTING by Abdul Khadeer, Vanaparthi Kiranmai, B. Suvarnamukhi, Ayaz Mohiuddin, Balika Mahesh, P M Suresh, J Arthy, Sasikumar A N, Sudersan Behera, Mohd Ayaz Uddin

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Enhanced Fault Localization in Multi-Terminal HVDC Systems Using Improved Gaussian Process Regression by Abha Pragati, Manohar Mishra, Pritam Bhowmik, Josep M. Guerrero, Debadatta Amaresh Gadanayak

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MQHOA algorithm with energy level stabilizing process by Peng WANG, Yan HUANG

“…An improved multi-scale quantum harmonic oscillator algorithm (MQHOA) with energy level stabilizing process was proposed analogizing to quantum harmonic oscillator's wave function. …”
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OPTIMIZING PROCESSOR WORKLOADS AND SYSTEM EFFICIENCY THROUGH GAME-THEORETIC MODELS IN DISTRIBUTED SYSTEMS by Merlan Telmanov, Zukhra Abdiakhmetova, Amandyk Kartbayev

“…Key results from this study highlight that while Nash Equilibrium fosters stability within the system, the adoption of optimal cooperative strategies significantly improves operational efficiency and minimizes transaction costs. …”
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Research on optimal selection of runoff prediction models based on coupled machine learning methods by Xing Wei, Mengen Chen, Yulin Zhou, Jianhua Zou, Libo Ran, Ruibo Shi

“…Employing a “decomposition-reconstruction” strategy combined with robust optimization algorithms enhances the performance of machine learning prediction models, thereby significantly improving the runoff prediction capabilities in watershed hydrological models.…”
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Adaptive predator prey algorithm for many objective optimization by Nikunj Mashru, Kanak Kalita, Lenka Čepová, Pinank Patel, Arpita, Pradeep Jangir

“…This paper presents the Many-Objective Marine Predator Algorithm (MaOMPA), an adaptation of the Marine Predators Algorithm (MPA) specifically enhanced for many-objective optimization tasks. …”
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Sustainable soil organic carbon prediction using machine learning and the ninja optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Marwa M. Eid, Marwa M. Eid, El-Sayed M. El-kenawy, El-Sayed M. El-kenawy

“…To address these challenges, we propose a novel integration of the Ninja Optimization Algorithm (NiOA) for simultaneous feature selection and hyperparameter optimization, aimed at enhancing both predictive accuracy and computational efficiency. …”
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Optimization model and heuristic solution method for multi-channel cooperative sensing in cognitive radio networks by Wei YANG, Dong-song BAN, Huan-zhong LI, Wen-hua DOU

“…An optimization model under the scenario where multi-channels are cooperatively sensed and used by multi-secondary users (SU) was proposed.The model aims to maximize the system throughput and optimizes the parameters including the sensing time and the weight coefficient of the sampling result of each SU for each channel,meanwhile the false access probability for each channel must not violate the given constraints.To solve this non-linear optimization model,a sequential parameters optimization method(SPO)was proposed.The method begins with deriving the lower bound of the objective function of the optimization model.Then it maximizes this lower bound by optimizing the weight coefficients through solving a series of sub-optimal problems using Lagrange method,and finally finding an optimized sensing time parameter by the golden search algorithm.Extensive experiments by simulations demonstrate the effectiveness of the proposed method and the advantage of the proposed model on improving the system throughput.…”
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Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications by João P. Castanheira, Beltran N. Arribas, Rui Melicio, Paulo Gordo, André R. R. Silva

“…This research employs a series of numerical simulations using methods such as Genetic Algorithms, Cultural Algorithms, and Artificial Immune Systems, with an emphasis on parameter tuning to optimize the reduced thermal model correlation. …”
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Landslide Displacement Prediction Model Based on Optimal Decomposition and Deep Attention Mechanism by Shuai Ren, Kamarul Hawari Ghazali, Yuanfa Ji, Samra Urooj Khan

“…Experimental results demonstrate that the proposed model significantly improves predictive performance, reducing the Root Mean Square Error (RMSE) by 60% compared to the traditional XGBoost model and by 33% compared to the Empirical Mode Decomposition-BiLSTM (EMD-BiLSTM) model. …”
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USING REINFORCEMENT LEARNING ALGORITHMS FOR UAV FLIGHT OPTIMIZATION by O. Dutsiak, V. Yuzevych

“…The study of the results of the functionality of the proposed algorithm was carried out in the environment of three-dimensional modeling. …”
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An Intelligent Fault Diagnosis Model for Rolling Bearings Based on IGTO-Optimized VMD and LSTM Networks by Xianglong Luo, Fengrong Yu, Jing Qian, Biao An, Nengpeng Duan

“…To address the issue of rolling bearing fault diagnosis, this paper proposes a novel model combining the Improved Gorilla Troop Optimization (IGTO) algorithm, Variational Mode Decomposition (VMD), Permutation Entropy (PE), and Long Short-Term Memory (LSTM) networks. …”
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Optimization Strategy of Multi-group Inspection Path of Distribution Equipment with Equipment Information Included by Chaoqiang CHEN, Hanyang GONG, Di ZHANG, Zhidan ZHANG, Jian LE

“…A distribution equipment information model is established, and a multi group distribution equipment routing optimization model is proposed to minimize the total routing cost, which is solved by genetic algorithm. …”
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Intelligent guarantee power supply decision method based on reinforcement learning algorithm by Milu Zhou, Huijie Sun, Tian Yang, Tingting Li, Qi Hou

“…Therefore, a decision model for guaranteeing power supply is constructed based on an improved proximal policy optimization algorithm, to study the intelligent guarantee power supply decision method. …”
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The Utilization of a Naïve Bayes Model for Predicting the Energy Consumption of Buildings by Behnam Sadaghat, Ali Javadzade Khiavi, Babak Naeim, Erfan Khajavi, Hadi Sadaghat, Amir Reza Taghavi Khanghah

“…It introduces a fusion of the African Vultures Optimization Algorithm (AVOA) and the Sand Cat Swarm Optimization (SCSO) with the Naïve Bayes (NB) model, aiming to elevate heating load prediction accuracy and streamline HVAC system optimization. …”
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An Inverse Modeling Multi-Objective Optimization Technique Based on Incremental Learning and Fuzzy Clustering by Gadallah Mohamed Abd Elaziz, Yasmine Abouelseoud, Sara H. Kamel

“…This paper aims to develop an inverse modeling MOEA based on decomposition that employs an incremental learning-based support vector regression (SVR) model, as an alternative to the Gaussian process model, in order to improve the quality of obtained solutions and speed up convergence of the algorithm. …”
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