Micro-Energy Grid Energy Utilization Optimization with Electricity and Heat Storage Devices Based on NSGA-III Algorithm by Junchao Yang, Li Li

“…This study built a multi-objective optimization model and used the NSGA-III algorithm to obtain a Pareto solution set. …”
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GAGAN: Enhancing Image Generation Through Hybrid Optimization of Genetic Algorithms and Deep Convolutional Generative Adversarial Networks by Despoina Konstantopoulou, Paraskevi Zacharia, Michail Papoutsidakis, Helen C. Leligou, Charalampos Patrikakis

“…In this paper, we propose a novel hybrid optimization method that integrates Genetic Algorithms (GAs) to improve the training process of Deep Convolutional GANs (DCGANs). …”
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An Efficient Multisensor Hybrid Data Fusion Approach Based on Artificial Neural Networks and Particle Swarm Optimization Algorithms by Luc Eyembe Ihonock, Jean-François Dikoundou Essiben, Benjamin Salomon Diboma, Joe Suk Yong

“…The preprocessing stage of data is also included in the suggested technique, which starts with the design of the data-collecting device and ends with a hybrid model algorithm. Particle swarm optimization and artificial neural network methods are combined in the hybrid algorithm. …”
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Optimal Design Method of a Hybrid CSP-PV Plant Based on Genetic Algorithm Considering the Operation Strategy by Rongrong Zhai, Ying Chen, Hongtao Liu, Hao Wu, Yongping Yang

“…In this paper, the operation strategy of the CSP-PV system is proposed for parabolic trough CSP system and PV system which are now commercially operated. Genetic algorithm is used to optimize the design of the system and calculate PV-installed capacity, battery capacity, and storage capacity of CSP system, making the system to achieve the lowest cost of electricity generation. …”
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Study on PID gain parameter optimization for a quadcopter under static wind turbulence using bio-inspired algorithms by Olukunle Kolawole Soyinka, Monica Ngunan Ikpaya, Lumi Luka

“…This study solves this problem using Bio-inspired algorithms to tune the controller gain. To determine the required PID controller gain parameters this paper utilizes a Simulink model of a quadcopter combined with the particle swarm optimization (PSO) algorithm and the cuckoo search algorithm (CSA) optimization respectively to minimize error in the attitude rate. …”
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A heuristic decomposition algorithm to optimally configure superconducting fault current limiters in Large-Scale power systems by Chuqin Wu, Mingbo Liu, Zihan Zhang, Min Xie

“…This paper establishes the optimal configuration model of fault current limiters considering transmission line switching and unit commitment, which is a complex Mixed-Integer Linear Programming (MILP) problem. …”
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Advanced KNN-based cost-efficient algorithm for precision localization and energy optimization in dynamic underwater sensor networks by Nadia Shamshad, Lei Wang, Kiran Saleem, Danish Sarwr, Salil Bharany, Ahmad Almogren, Jaeyoung Choi, Ateeq Ur Rehman, Ayman Altameem

“…Results demonstrate notable improvements in localization accuracy by optimizing the localization error rate from 4.59m to $$3.88 \times 10^{-8}$$ m, Reducing localization energy consumption rate 0.0045J in addition for the first time we have also computed the localization Time cost rate which is 0.06762s. we assumed that in real-time and in NS-3 simulations on the Aqua-sim model indicate communication speed at 1500m/s. …”
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Optimal sizing and siting of energy storage systems based on power grid vulnerability analysis: a trilevel optimization model by Zhenghui Zhao, Yingying Shang, Buyang Qi, Yang Wang, Qian Zhang

“…The middle-level employs an improved particle swarm optimization algorithm to determine the optimal capacity and power configuration of BESS, aiming to maximize its equivalent annual revenue. …”
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Parametric-modeling-based multi-objective thermoelastic optimization of rudder structures by Shi Guanghui, Bao Yuhao, Wu Wenhua, Guo Guiqiang, Lin Ye, Zhang Xiaopeng, Tao Ran

“…By leveraging commercial software and established optimization algorithms, this method can be adapted to other thermoelastic optimization challenges through refinements such as parametric modeling and multi-objective optimization functions. …”
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Improving the area-preserving parameterization of rational Bézier surfaces by rational bilinear transformation by Xiaowei Li, Yingjie Wu, Yaohui Sun, Xin Chen, Yanru Chen, Yi-jun Yang

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Prediction method of gas emission in working face based on feature selection and BO-GBDT by MA Wenwei

“…In fact, redundant or irrelevant features reduced the model's prediction accuracy. To further improve performance, five hyperparameter optimization algorithms were applied to the GBDT model. …”
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Automatic Generation Technology of Safety Measures for Digital Substation Based on Improved Support Vector Machine by Yabing YAN, Xu CHU, Haolong XIAO, Wenwu LIANG, Hui LI, Zhenxing XIA

“…Firstly, construct a secondary circuit model and equipment model based on adjacency matrix, and further integrate the secondary security measure rule library to form a sample dataset; Secondly, support vector machines were used to classify secondary security measures, and bacterial foraging algorithms were introduced to optimize penalty factors and kernel parameters, effectively improving the training effectiveness of the automatic generation model for security measures; Finally, the effectiveness of the proposed method was verified through numerical examples.…”
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A Genetic Algorithm Approach to Adaptive Learning: Enhancing Learner Satisfaction and Performance by Anju Kalwar, Deepika Shekhawat, Sandeep Joshi, Amit Kumar Bairwa

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A Study on the Design of a New Three-Dimensional Seismic Isolation Bearing Based on an Improved Genetic Algorithm for Bridge Engineering by Ying Sheng, Zhenchao Yang, Yu Meng, Bin Jia

“…However, passive seismic isolation devices, due to their non-adjustable performance parameters, struggle to achieve effective seismic isolation across a wide frequency range of 0 Hz to 20 Hz in response to random and varying seismic loads. (2) Methods: The sensitivity of the design parameters of the seismic isolation bearing was analyzed using the optimization center gradient method, and an improved genetic algorithm was employed to quickly optimize and obtain the optimal design parameters. (3) Results: The effectiveness of the three-dimensional seismic isolation bearing was validated through experiments. (4) Conclusions: The multi-factor sensitivity analysis approach used in this study for designing novel isolation bearings is applicable not only to seismic design in bridges but also serves as a reference for parameter design in isolation bearings requiring medium to high precision in seismic performance.…”
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Integrating evolutionary algorithms and enhanced-YOLOv8 + for comprehensive apple ripeness prediction by Yuchi Li, Zhigao Wang, Aiwei Yang, Xiaoqi Yu

“…For structured text data, support vector regression (SVR) models optimized using the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), and Sparrow Search Algorithm (SSA) were utilized to predict apple ripeness, with the WOA-optimized SVR demonstrating exceptional generalization capabilities. …”
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Optimizing Human-Centric Warehouse Operations: A Digital Twin Approach Using Dynamic Algorithms and AI/ML by Erhan Arslan

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GA-VAE: Enhancing Local Feature Representation in VQ-VAE Through Genetic Algorithm-Based Token Optimization by Jinghui Jiang, Dongjoon Kim, Bohyoung Kim, Yeong-Gil Shin

“…This paper introduces GA-VAE, a fine-tuning framework that enhances local feature representation in pre-trained Vector Quantized-VAE (VQ-VAE) models through genetic algorithm-based optimization. …”
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Joint energy efficiency and spectral efficiency optimization algorithm for UDN under the restriction of interference threshold and backhaul capacity by Xuanli WU, Xu CHEN

“…Aiming at the scenarios which consider the constraint of backhaul capacity restriction and interference threshold in ultra-dense networks (UDN),an integer linear programming (ILP) and Lagrangian dual decomposition (LDD) based joint optimization algorithm of energy efficiency and spectrum efficiency was proposed.In the proposed algorithms,the user association problem with the constraint of limited backhaul capacity was modelled as an ILP problem and then finished the connection between the user and the base station of microcell by solving this problem with dynamic programming method.Therefor,Lagrangian dual decomposition (LDD) was applied in an iteration algorithm for spectrum resource allocation and power allocation.The simulation results show that compared with traditional schemes,the proposed algorithm can significantly improve the energy efficiency and spectrum efficiency of system and use the microcell’s load capacity more efficiently.…”
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Joint energy efficiency and spectral efficiency optimization algorithm for UDN under the restriction of interference threshold and backhaul capacity by Xuanli WU, Xu CHEN

“…Aiming at the scenarios which consider the constraint of backhaul capacity restriction and interference threshold in ultra-dense networks (UDN),an integer linear programming (ILP) and Lagrangian dual decomposition (LDD) based joint optimization algorithm of energy efficiency and spectrum efficiency was proposed.In the proposed algorithms,the user association problem with the constraint of limited backhaul capacity was modelled as an ILP problem and then finished the connection between the user and the base station of microcell by solving this problem with dynamic programming method.Therefor,Lagrangian dual decomposition (LDD) was applied in an iteration algorithm for spectrum resource allocation and power allocation.The simulation results show that compared with traditional schemes,the proposed algorithm can significantly improve the energy efficiency and spectrum efficiency of system and use the microcell’s load capacity more efficiently.…”
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Geological disaster risk assessment based on remote sensing, GIS, and a multi-strategy improved GWO-optimized Elman neural network by Kuayue Chen, Fuhong Bai, Ting Wang, Yingmei Su, Baoyun Wang

“…To improve the accuracy and intelligence of geological disaster risk assessment, this study proposes a novel method based on a Multi-strategy Improved Grey Wolf Optimization algorithm combined with the Elman Neural Network (MIGWO-Elman). …”
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