A Novel Prediction Model for the Sales Cycle of Second-Hand Houses Based on the Hybrid Kernel Extreme Learning Machine Optimized Using the Improved Crested Porcupine Optimizer by Bo Yu, Deng Yan, Han Wu, Junwu Wang, Siyu Chen

“…For this reason, this paper develops a prediction model of the second-hand housing sales cycle based on the hybrid kernel extreme learning machine (HKELM) optimized using the Improved Crested Porcupine Optimizer (CPO), which has achieved rapid and accurate prediction. …”
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Determining Appropriate Strategy for Managing the Energy Production System in the Presence of Renewable Energy Sources and Improved Intelligence Method by Amir Sohel, Md Faisal Bin Ashab

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Two-Layer Optimal Scheduling and Economic Analysis of Composite Energy Storage with Thermal Power Deep Regulation Considering Uncertainty of Source and Load by Chao Xing, Jiajie Xiao, Xinze Xi, Jingtao Li, Peiqiang Li, Shipeng Zhang

“…The upper layer takes pumped storage as the optimization goal to improve net load fluctuation and the optimal peak load benefit; the lower layer takes the system’s total peak load cost as the optimization goal and obtains a day-before scheduling plan for the energy storage system, using an improved gray wolf algorithm to process it. …”
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An innovative coverage optimization method for smart information monitoring in agricultural IoT using the multi-strategy Pelican optimization algorithm by Wei Chen, Qike Cao, Bingyu Cao, Bo Jin

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Short-Term Power Load Prediction Based on Level Processing Method and Improved GWO Algorithm by Yuntong Li

“…To address this issue, this study introduces level processing method and improved grey wolf genetic algorithm to predict short-term power load and optimize the power load prediction accuracy. …”
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Metaparameter optimized hybrid deep learning model for next generation cybersecurity in software defined networking environment by C. Labesh Kumar, Suresh Betam, Denis Pustokhin, E. Laxmi Lydia, Kanchan Bala, Rajanikanth Aluvalu, Bhawani Sankar Panigrahi

“…For the DDoS attack classification process, the attention mechanism with convolutional neural network and bidirectional gated recurrent units (CNN-BiGRU-AM) is employed. To ensure optimal performance of the CNN-BiGRU-AM model, hyperparameter tuning is performed by utilizing the seagull optimization algorithm (SOA) model to enhance the efficiency and robustness of the detection system. …”
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Network Security Situational Awareness Based on Improved Particle Swarm Algorithm and Bidirectional Long Short-Term Memory Modeling by Peng Zheng, Yun Cheng, Wei Zhu, Bo Liu, Shuhong Liu, Shijie Wang, Jinyin Bai

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Evaluation Modeling of Electric Bus Interior Sound Quality Based on Two Improved XGBoost Algorithms Using GS and PSO by Enlai ZHANG, Yi CHEN, Liang SU, Ruoyu ZHONGLIAN, Xianyi CHEN, Shangfeng JIANG

“…Aiming at the practical application requirements of high-precision modeling of acoustic comfort in vehicles, this paper presented two improved extreme gradient boosting (XGBoost) algorithms based on grid search (GS) method and particle swarm optimization (PSO), respectively, with objective parameters and acoustic comfort as input and output variables, and established three regression models of standard XGBoost, GS-XGBoost, and PSO-XGBoost through data training. …”
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Resilience-Improving Based Optimization of Post-Disaster Emergency Maintenance Strategy for Transmission Networks by Haiping LIANG, Haoyan SHI, Yan WANG, Yingpei LIU, Xinming WANG

“…An improved particle swarm optimization (PSO) algorithm is proposed for the optimization model, which uses such methods as the multi-dimensional indefinite length coding, sub-group collaborative optimization, and Monte-Carlo-simulation-based fitness evaluation to improve the standard PSO algorithm. …”
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Bio inspired optimization techniques for disease detection in deep learning systems by A. Ashwini, Vanajaroselin Chirchi, S. Balasubramaniam, Mohd Asif Shah

“…This research endeavors to elucidate the integration of bio-inspired optimization techniques that improve disease diagnostics through deep learning models. …”
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Grey modeling method for approximate exponential sequence of optimizing initial condition by Yun YUE, Guangyue LU

“…Grey GM（1,1）prediction method is only suitable for the prediction model of the original sequence which satisfies the characteristic of the approximate exponential through the accumulated generating operation.In order to widen the application range of the traditional grey prediction model,a new method,dubbed DGM（1,1,c,β）model（direct grey model）,was proposed to improve the accuracy of grey GM（1,1）prediction by optimizing initial conditions.DGM（1,1,c,β）model was established for the original sequence conforming to the approximate exponential and the model parameters were obtained by the particle swarm optimization algorithm.Both the simulation and analysis of the example demonstrate that the proposed method is more effective and practical.…”
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The Impact of Different Parallel Strategies on the Performance of Kriging-Based Efficient Global Optimization Algorithms by Hang Fu, Qingyu Wang, Takuji Nakashima, Rahul Bale, Makoto Tsubokura

“…A parallel efficient global optimization (EGO) algorithm with a pseudo expected improvement (PEI) multi-point sampling criterion, proposed in recent years, is developed to adapt the capabilities of modern parallel computing power. …”
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Research on Vehicle Route Optimization for Half-Open Multi-Energy Urban Distribution Considering Order Priority by Mingxuan Zhang

“…Aiming at the current problems of increasingly serious tailpipe pollution of urban distribution vehicles and irrational distribution route planning, we construct a fuel-electric hybrid multi-trip multi-center half-open joint distribution vehicle routing optimization model (F-EHOMTMDVRPOPTW) considering order priority and fuzzy time window, and introduce Tent chaotic mapping combined with an improved discrete sparrow search algorithm (DSSA) with stochastic key encoding strategy for solving. …”
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A Novel Ship Fuel Sulfur Content Estimation Method Using Improved Gaussian Plume Model and Genetic Algorithms by Chao Wang, Hao Wu, Nini Wang, Zhirui Ye

“…The emission source intensity inversion was formulated as an unconstrained multi-dimensional optimization problem, solved using genetic algorithms. …”
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A new type of sustainable operation method for urban rail transit: Joint optimization of train route planning and timetabling by Guorong Fan, Chao Li, Xinyun Shao, Fangzheng Zhen, Yao Huang

“…A linearization method for the model is proposed. To solve large-scale problems, an improved adaptive large neighborhood search algorithm (ALNS) is designed accordingly. …”
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DGCA3QM: DESIGN OF A DUAL GENETIC ALGORITHM BASED AUTOREGRESSION MODEL FOR CORRELATIVE PREDICTION OF AIR QUALITY METRICS by Harna M. Bodele, G. M. Asutkar, Kiran G. Asutkar

“…Due to incorporation of dual bioinspired optimizers with autoregressive correlation, the model is able to improve prediction accuracy by 8.5%, precision by 4.9%, recall by 1.5%, while reducing computational delay by 3.4% when compared with standard air quality analysis models. …”
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Prediction of Lithium-Ion Battery State of Health Using a Deep Hybrid Kernel Extreme Learning Machine Optimized by the Improved Black-Winged Kite Algorithm by Juncheng Fu, Zhengxiang Song, Jinhao Meng, Chunling Wu

“…Addressing the non-linear and non-stationary characteristics of battery capacity sequences, a novel method for predicting lithium battery SOH is proposed using a deep hybrid kernel extreme learning machine (DHKELM) optimized by the improved black-winged kite algorithm (IBKA). …”
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Enhanced hunger games search algorithm that incorporates the marine predator optimization algorithm for optimal extraction of parameters in PEM fuel cells by Mohamed Issa, Mohamed Abd Elaziz, Sameh I. Selem

“…Abstract This article introduces a novel optimization approach to improve the parameter estimation of proton exchange membrane fuel cells (PEMFCs), which are critical for diverse applications but are challenging to model due to their nonlinear behavior. …”
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RM-MOCO: A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention by Huiqing Wei, Fei Han, Qing Liu, Henry Han

“…In this paper, following the idea of decomposition strategy and neural combinatorial optimization, a novel fast-solving model for MOCO based on retention is proposed. …”
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Ultra Short-Term Charging Load Forecasting Based on Improved Data Decomposition and Hybrid Neural Network by Shaoyang Yin, Zhaohui Chen, Wanyuan Liu, Zhiwen Su

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