Evolutionary Cost Analysis and Computational Intelligence for Energy Efficiency in Internet of Things-Enabled Smart Cities: Multi-Sensor Data Fusion and Resilience to Link and Devi... by Khalid A. Darabkh, Muna Al-Akhras

“…When compared to the most recent and relevant protocols, including the Particle Swarm Optimization-based energy-efficient clustering protocol (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), and Novel PSO-based Protocol (NPSOP), our approach achieves very promising results. …”
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Assimilating Satellite-Based Biophysical Variables Data into AquaCrop Model for Silage Maize Yield Estimation Using Water Cycle Algorithm by Elahe Akbari, Ali Darvishi Boloorani, Jochem Verrelst, Stefano Pignatti

“…Based on our proposed workflow in previous studies, a Gaussian process regression–particle swarm optimization (GPR-PSO) algorithm and global sensitivity analysis were applied to retrieve the fCover and biomass from Sentinel-2 satellite data and to identify the most sensitive parameters in the AquaCrop model, respectively. …”
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A carbon aware ant colony system for the sustainable generalized traveling salesman problem by Marina Lin, Laura P. Schaposnik

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An improved beetle antennae search algorithm and its application in coverage of wireless sensor networks by Biao Yin, Liping Mo, Wei Min, Shan Li, Cunwei Yu

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The close-open mixed-fleet electric vehicle routing problem by Themistoklis Stamadianos, Nikolaos A. Kyriakakis, Magdalene Marinaki, Yannis Marinakis

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Application of Simulated Annealing Algorithm in the Construction of Online Examination System for Tax Law Courses by da Pan

“…Experimental results demonstrate that SA achieves superior performance compared to genetic algorithms (GA), greedy approaches, ant colony optimization (ACO), and particle swarm optimization (PSO). …”
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Predictive analytics of complex healthcare systems using deep learning based disease diagnosis model by Muhammad Kashif Saeed, Alanoud Al Mazroa, Bandar M. Alghamdi, Fouad Shoie Alallah, Abdulrhman Alshareef, Ahmed Mahmud

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Multi-step Prediction of Monthly Sediment Concentration Based on WPT-ARO-DBN/WPT-EPO-DBN Model by GAO Xuemei, CUI Dongwen

“…Accurate multi-step sediment concentration prediction is of significance for regional soil erosion control,flood control and disaster reduction.To improve the multi-step prediction accuracy of sediment concentration and the prediction performance of the deep belief network (DBN),this paper proposes a multi-step prediction model of monthly sediment concentration by combining the artificial rabbit optimization (ARO) algorithm,eagle habitat optimization (EPO) algorithm,and DBN based on wavelet packet transform (WPT).The model is validated using time series data of monthly sediment concentration from Longtan Station in Yunnan Province.Firstly,WPT is employed to decompose the time series data of the monthly sediment concentration of the case in three layers,and eight more regular subsequence components are obtained.Secondly,the principles of ARO and EPO algorithms are introduced,and hyperparameters such as the neuron number in the hidden layer of DBN are optimized by ARO and EPO.Meanwhile,WPT-ARO-DBN and WPT-EPO-DBN prediction models are built,and WPT-PSO (particle swarm optimization)-DBN and WPT-DBN are constructed for comparative analysis.Finally,four models are adopted to predict each subsequence component,and the predicted values are superimposed to obtain the multi-step prediction results of the final monthly sediment concentration.The results are as follows.① WPT-ARO-DBN and WPT-EPO-DBN models have satisfactory prediction effects on the monthly sediment concentration of the case from one step ahead to four steps ahead.This yields sound prediction results for five steps ahead.The prediction effect for six steps ahead and seven steps ahead is average,and the prediction accuracy for eight steps ahead is poor and cannot meet the prediction accuracy requirements.② The multi-step prediction performance of WPT-ARO-DBN and WPT-EPO-DBN models is superior to WPT-PSO-DBN models and far superior to WPT-DBN models,with higher prediction accuracy,better generalization ability,and larger prediction step size.③ ARO and EPO can effectively optimize DBN hyperparameters,improve DBN prediction performance,and have better optimization effects than PSO.Additionally,WPT-ARO-DBN and WPT-EPO-DBN models can give full play to the advantages of WPT,new swarm intelligence algorithms and the DBN network and improve the multi-step prediction accuracy of monthly sediment concentration,and the prediction accuracy decreases with the increasing prediction steps.…”
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Research on the connectivity reliability analysis and optimization of natural gas pipeline network based on topology by Xiuxuan Yang, Kun Chen, Minghui Liu

“…Case studies on a regional pipeline network (89 nodes, 98 segments) demonstrate that loop structures exhibit 25.7% higher average reliability ( $$\:{R}_{j}$$ = 0.87792) than branch nodes (v79: $$\:{R}_{j}$$ =0.60933). The AGA-driven optimization increases system-wide connectivity reliability ( $$\:{R}_{SU}$$ ) from 0.03 to 0.247 by strategically adding redundant pipelines (v71–v77), outperforming particle swarm optimization (PSO) by 65%. …”
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Enhanced probabilistic prediction of pavement deterioration using Bayesian neural networks and cuckoo search optimization by Feng Xiao, Biying Shi, Jie Gao, Huapeng Chen, Di Yang

“…Finally, based on the data from the pavement management system in Shanxi Province, it was verified that the CS-BNN model outperforms the genetic algorithm-BNN, particle swarm optimization-BNN, and BNN models in terms of the two metrics. …”
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KO algorithm-based bi-level optimal scheduling of electricity-carbon-hydrogen coupling systems with flexible resources for renewable energy integration by Jing Liu, Zicheng Guo, Yalong Li

“…Moreover, the Kepler optimization (KO) algorithm is applied and benchmarked against flower pollination (FP) algorithm and particle swarm optimization (PSO) algorithm. …”
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Structural Damage Identification Using PID-Based Search Algorithm: A Control-Theory Inspired Application by Kuang Shi, Tingting Sun

“…The Relative Frequency Change Rate (RFCR) and Modal Assurance Criterion (MAC) were calculated as objective functions for PSA iteration; comparative studies were then conducted against Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA) in terms of damage identification accuracy, computational efficiency, and noise robustness. …”
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Optimal Configuration of Multi-microgrid System with Multi-agent Joint Investment Based on Stackelberg Game by Ruiyuan PAN, Zhong TANG, Chenhao SHI, Minjie WEI, An LI, Weiyang DAI

“…Then, a Stackelberg game model is built to minimize the payoff function of the multi-microgrid system and maximize the revenue of distribution networks separately. In addition, an algorithm combining the adaptive genetic algorithm and particle swarm optimization is proposed to solve the optimal configuration of distributed power in the multi-microgrid system. …”
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Fusion of Visible and Infrared Images Using a Reinforcement Learning System Based on Fuzzy Logic and Convolution Optimized with Wild Horse Algorithm by Mahvash Zarimeidani, Amir Amirabadi, Nasrin Amiri, Iman Ahanian, Siavash Es’haghi

“…This hybrid reinforcement learning system was optimized using algorithms including wild horse optimization (WHO), genetic algorithm (GA), and particle swarm optimization (PSO) to improve specific fusion metrics such as image correlation, similarity coefficient, image entropy, and signal-to-noise ratio. …”
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Research on cooperative control strategy for high efficiency and energy saving in virtually coupled train sets based on two-layer optimization by JIANG Sidun, FENG Jianghua, ZHANG Zhengfang, SHI Ke, LUO Qinyang

“…The lower layer concentrates on energy-saving optimization, establishing an objective function for energy saving and utilizing a multi-objective particle swarm algorithm to optimize cruising curves. …”
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Optimized ensemble learning for non-destructive avocado ripeness classification by Panudech Tipauksorn, Prasert Luekhong, Minoru Okada, Jutturit Thongpron, Chokemongkol Nadee, Krisda Yingkayun

“…Five machine learning models Random Forest, Decision Tree, XGBoost, Gradient Boosting, and Gaussian Mixture Model were trained separately and then merged into an ensemble. Four algorithms were used to optimize the model weight distribution: Bayesian Optimisation, Differential Evolution, Particle Swarm Optimisation, and Grid Search. …”
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Optimizing Outdoor Micro-Space Design for Prolonged Activity Duration: A Study Integrating Rough Set Theory and the PSO-SVR Algorithm by Jingwen Tian, Zimo Chen, Lingling Yuan, Hongtao Zhou

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Optimizing LoRaWAN Gateway Placement in Urban Environments: A Hybrid PSO-DE Algorithm Validated via HTZ Simulations by Kanar Alaa Al-Sammak, Sama Hussein Al-Gburi, Ion Marghescu, Ana-Maria Claudia Drăgulinescu, Cristina Marghescu, Alexandru Martian, Nayef A. M. Alduais, Nawar Alaa Hussein Al-Sammak

“…This study investigates how to optimize the placement of LoRaWAN gateways by using a combination of Particle Swarm Optimization (PSO) and Differential Evolution (DE). …”
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Optimized deep neural network architectures for energy consumption and PV production forecasting by Eghbal Hosseini, Barzan Saeedpour, Mohsen Banaei, Razgar Ebrahimy

“…This paper introduces a novel hybrid optimization approach that integrates Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to enhance the DNN architecture for more accurate energy forecasting. …”
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Allocation of Interline Power Flow Controller-Based Congestion Management in Deregulated Power System by Muhammad Safdar Sial, Qinghua Fu, Talles Vianna Brugni

“…By examining the obtained results, the performance of the proposed algorithm is better than the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. …”
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