Prediction of the cutting tool wear during dry hard turning of AISI D2 steel by using models based on Learning process and GA polyfit by Khaled Djellouli, Kamel Haddouche, Mostefa Belarbi, Zoubir Aich

“…For this purpose, the current research focuses on the development of predictive models of flank wear based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Polynomial Fit using Genetic Algorithm (GAPOLYFITN). The simulation process involves considering input variables including feed (f), cutting speed (Vc), and cutting time (tc); the output is the flank wear (VB). …”
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Design of a novel Cu-Cr-X alloy with high strength and high electrical conductivity based on mechanical learning by Yunqing Zhu, Yicheng Cao, Lijun Peng, Qian Yu, Zhen Yang, Zengde Li, Junsheng Wu, Haofeng Xie

“…The Cu-Cr-Zr-Mg-Ti alloy with superior properties was optimally designed by genetic algorithm from the massive solutions, which the experimental tensile strength and conductivity reached 668 MPa and 71.5 %IACS, respectively. …”
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Development of a Mobile Application Platform for Self-Management of Obesity Using Artificial Intelligence Techniques by Sylvester M. Sefa-Yeboah, Kwabena Osei Annor, Valencia J. Koomson, Firibu K. Saalia, Matilda Steiner-Asiedu, Godfrey A. Mills

“…In this work, we present an artificial intelligence- (AI-) based application that is driven by a genetic algorithm (GA) as a potential tool for tracking a user’s energy balance and predicting possible calorie intake required to meet daily calorie needs for obesity management. …”
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Mathematical modelling and optimization of cutting conditions in turning operation on MDN 350 steel with carbide inserts by Syed Adil, A. Krishnaiah, D. Srinivas Rao

“…Artificial neural networks technique, which is well known for its versatility to model linear as well as non-linear data, is used to express the surface roughness as a function of tool geometrical variables. Genetic Algorithm, which is an advanced optimization technique known for its intricate search for optimal solutions, is used for optimizing surface roughness with optimal combination of the geometrical parameters. …”
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MORKO: A Multi-objective Runge–Kutta Optimizer for Multi-domain Optimization Problems by Kanak Kalita, Pradeep Jangir, Sundaram B. Pandya, Ahmed Ibrahim Alzahrani, Fahad Alblehai, Laith Abualigah, Absalom E. Ezugwu

“…The efficiency of MORKO is compared against renowned algorithms like the multi-objective marine predicator algorithm (MOMPA), multi-objective gradient-based optimizer (MOGBO), multi-objective evolutionary algorithm based on decomposition (MOEA/D), and non-dominated sorting genetic algorithm (NSGA-II) on several test benchmarks such as ZDT, DTLZ, constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design (brushless DC wheel motor, safety isolating transformer, helical spring, two-bar truss, welded beam, disk brake, tool spindle and cantilever beam) problems. …”
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Inversion of Water Quality Parameters from UAV Hyperspectral Data Based on Intelligent Algorithm Optimized Backpropagation Neural Networks of a Small Rural River by Manqi Wang, Caili Zhou, Jiaqi Shi, Fei Lin, Yucheng Li, Yimin Hu, Xuesheng Zhang

“…Again, based on the screened features, a back-propagation neural network (BPNN) model optimized using a mixture of the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm was established as a means of estimating water quality parameter concentrations. …”
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An Energy-Balanced Path Planning Algorithm for Multiple Ferrying UAVs Based on GA by Lisong Wang, Xiaoliang Zhang, Pingyu Deng, Jiexiang Kang, Zhongjie Gao, Liang Liu

“…Further, we model the energy-balanced path-planning problem for multiple ferrying UAVs, which actually is a multiobject optimization problem of minimizing the planned path length and minimizing the variance of all ferrying UAVs’ energy-factor. Based on the genetic algorithm, we design and implement an energy-balanced path planning algorithm (EMTSPA) for multiple ferrying UAVs to solve this multiobject optimization problem. …”
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Optimization of a photovoltaic/wind/battery energy-based microgrid in distribution network using machine learning and fuzzy multi-objective improved Kepler optimizer algorithms by Fude Duan, Mahdiyeh Eslami, Mohammad Khajehzadeh, Ali Basem, Dheyaa J. Jasim, Sivaprakasam Palani

“…Also, the MOIKOA superior capability is validated in comparison with the multi-objective conventional Kepler optimization algorithm, multi-objective particle swarm optimization, and multi-objective genetic algorithm in problem-solving. The findings are cleared that microgrid multi-objective optimization in the distribution network considering forecasted data based on the MLP-ANN causes an increase of 3.50%, 2.33%, and 1.98%, respectively, in annual energy losses, voltage deviation, and the purchased power cost from the HMG compared to the real data-based optimization. …”
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Patch-Level and Neighborhood-Dependency Spatial Optimization Method (PNO): Application to Urban Land-Use Planning to Facilitate Both Socio-Economic and Environmental Development in... by Yuhan Cheng, Xiuyuan Zhang, Qi Zhou, Xiaoyan Dong, Shihong Du

“…The PNO represents land-use patterns in a graph structure, quantifies land-use patterns’ impacts on the population, economy, and land surface temperature, defines the spatiotemporal constraints of land-use optimization considering neighborhood-dependency and optimization sequences, and finally optimizes land uses and their spatial layouts based on a multi-objective genetic algorithm. Experiments were conducted in the urban area of Beijing, and the results suggested that, after optimization, the population and GDP can be improved by 667,323 people (4.72%) and USD 10.69 billion in products (2.75%) in the study area; meanwhile, the land surface temperature can be reduced by 0.12 °C (−0.32%). …”
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Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor... by Mehdi Tlija, Muhammad Sana, Anamta Khan, Sana Hassan, Muhammad Umar Farooq

“…The optimized architectures are used as prediction models to a non-sorting genetic algorithm (NSGA-II) to determine the optimal compromise among all conflicting responses. …”
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Management of Uncertainty by Statistical Process Control and a Genetic Tuned Fuzzy System by Stephan Birle, Mohamed Ahmed Hussein, Thomas Becker

“…Additionally, in order to stay within the process corridors, a genetic algorithm was applied to tune the input and output fuzzy sets of a preliminarily parameterized fuzzy controller. …”
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Optimization of power system load forecasting and scheduling based on artificial neural networks by Jiangbo Jing, Hongyu Di, Ting Wang, Ning Jiang, Zhaoyang Xiang

“…Economic cost analysis further demonstrates that the PSO scheduling strategy achieves significantly lower costs at most time points compared to the Genetic Algorithm (GA) and Simulated Annealing (SA) strategies, with the cost being 689.17 USD in the first hour and 2214.03 USD in the fourth hour, both lower than those of GA and SA. …”
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Prediction of compressive strength and characteristics analysis of semi-flexible pavement desert sand grouting material based upon hybrid-BP neural network by Wenbang Zhu, Yuhang Li, Xiumei Zheng, Enze Hao, Dali Zhang, Zhen Wang

“…To precisely obtain DSGM exhibiting exceptional mechanical properties, the Backpropagation Neural Network (BPNN) model was optimized through the utilization of Particle Swarm Optimization (PSO), Sparrow Search Algorithm (SSA), and Genetic Algorithm (GA). Relationships between water-cement (w/c) ratio, desert sand (DS) content, fly ash (FA) content, bentonite (BT) content, and superplasticizer (SP) dosage were established in relation to compressive strength. …”
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Spatiotemporal Evolution Characteristics of Fluid Flow through Large-Scale 3D Rock Mass Containing Filling Joints: An Experimental and Numerical Study by Qian Yin, Hongwen Jing, Tantan Zhu, Lizhou Wu, Haijian Su, Liyuan Yu

“…The water pressure contour planes distributed along the flowing path generally transfer from a “long funnel” shape to a “short funnel” shape before reaching a series of parallel pressure planes perpendicular to the joint direction. By using the genetic algorithm, the coupling influences of these factors on the pore water pressure and flow velocity were investigated, and the decision parameters were optimized. …”
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Multi-thermal recovery layout for a sustainable power and cooling production by biomass-based multi-generation system: Techno-economic-environmental analysis and ANN-GA optimizatio... by Ma Zihao, Naeim Farouk, Pradeep Kumar Singh, Azher M. Abed, Sarminah Samad, Samah G. Babiker, Iskandar Shernazarov, Ahmed Hendy, Ahmad Almadhor, Belgacem Bouallegue, Abdul Rahman Afzal

“…A novel approach combining artificial neural networks (ANN) with a non-dominated sorting genetic algorithm II (NSGA-II) has been developed to optimize the system, substantially reducing computational time and costs associated with system performance analysis. …”
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Sustainable synthesis and advanced optimization of Prosopis juliflora biomass catalyst for efficient biodiesel production and environmental impact assessment by Thota S S Bhaskara Rao, Manikandaraja Gnanaprakasam, Rajayokkiam Manimaran, Dhinesh Balasubramanian, Utku Kale, Artūras Kilikevičius

“…Abstract The present research focuses on developing an innovative biochar-based heterogeneous catalyst from Prosopis Juliflora biomass waste using response surface methodology and genetic algorithm (GA) to optimize pyrolysis parameters, achieving a 46.31% PJBC yield from 60 mg of biomass at 790 °C for 60 min. …”
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Time Series Prediction of COD_Mn in Dianchi Lake Based on Data Decomposition and NARX Optimization by WANG Yongshun, CUI Dongwen

“…Comparative analyses are made with WPT-particle swarm optimization (PSO) - NARX, WPT-genetic algorithm (GA) - NARX, WPT-NARX, SHIO-NARX, WPT-SHIO extreme learning machine (ELM), and WPT-SHIO-BP neural network models. …”
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Study on Intelligent Classing of Public Welfare Forestland in Kunyu City by Meng Sha, Hua Yang, Jianwei Wu, Jianning Qi

“…Unlike previous approaches, the SVM model is optimized with Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to automatically determine classification parameters, overcoming the limitations of manual rule-based methods. …”
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Sustainability metrics targeted optimization and electric discharge process modelling by neural networks by Muhammad Sana, Muhammad Asad, Muhammad Umar Farooq, Mehdi Tlija, Rodolfo Haber

“…Moreover, multi-response optimization through the non-dominated sorting genetic algorithm (NSGA-II) has also been performed. The magnitudes of MRRCT, SRCT, and SECCT obtained by multi-response optimization are 64.82%, 27.45%, and 46.60% are better than the values obtained by un-optimized settings of CT brass electrodes. …”
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Prediction of time-dependent bearing capacity of concrete pile in cohesive soil using optimized relevance vector machine and long short-term memory models by Jitendra Khatti, Mohammadreza Khanmohammadi, Yewuhalashet Fissha

“…The comparison of 33 RVM and 3 LSTM models reveals that the genetic algorithm-optimized Gaussian kernel function-based SRVM model, i.e., UBC7, has been recognized as the optimal performance model with the RMSE = 146.3962 kPa, PI = 1.85, VAF = 94.60, NMBE = 30.1379 kPa, MAE = 105.7009 kPa, and R = 0.9727, close to the ideal values. …”
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