A novel swarm intelligence-based approach for solving optimal power flow problems in modern power systems by Haewon Byeon, Wajdi Alghamdi, Munni Evin, M. Sucharitha, D. David Neels Ponkumar, A. Prakash, J. Sunil

“…In order to solve the optimum power flow difficulties related to the C-UPFC, this research suggested an Improved Pelican optimization algorithm (IPOA). Install the superior flexible AC transmission system (FACTS) in accordance with the transmission line (TL) in a series configuration to provide independent voltage control that combines power flow regulation. …”
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A multi-objective optimization algorithm-based capacity scheduling method for photovoltaic power hybrid energy storage systems by Shuaijie Wang, Xin Guan, Shu Liu

“…In this study, the combination of crossover algorithm and particle swarm optimization—crossover algorithm-particle swarm optimization (CS-PSO) algorithm—to optimize photovoltaic hybrid energy storage scheduling, improving global search and convergence speed, is discussed. …”
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The quick crisscross sine cosine algorithm for optimal FACTS placement in uncertain wind integrated scenario based power systems by Sunilkumar P. Agrawal, Pradeep Jangir, Laith Abualigah, Sundaram B. Pandya, Anil Parmar, Absalom E. Ezugwu, Arpita, Aseel Smerat

“…The Quick Crisscross Sine Cosine Algorithm (QCSCA) was developed to address the challenges of solving the Optimal Power Flow (OPF) problem in power systems that integrate renewable energy sources and Flexible AC Transmission Systems (FACTS) devices. …”
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OptiSGD-DPWGAN: Integrating Metaheuristic Algorithms and Differential Privacy to Improve Privacy-Utility Trade-Off in Generative Models by Alshaymaa Ahmed Mohamed, Yasmine N. M. Saleh, Ayman A. Abdel-Hamid

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Improved swin transformer-based thorax disease classification with optimal feature selection using chest X-ray. by Nadim Rana, Yahaya Coulibaly, Ayman Noor, Talal H Noor, Md Imran Alam, Zeba Khan, Ali Tahir, Mohammad Zubair Khan

“…To further improve feature selection, we utilize the Chaotic Whale Optimization (ChWO) Algorithm, which optimally selects the most relevant attributes from the extracted features. …”
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Optimal design of hollow conductor for high‐speed synchronous motor exploiting adaptive‐sampling radial basis function algorithm by Ruiye Li, Peng Cheng, Hai Lan

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Anti-Collision Path Planning and Tracking of Autonomous Vehicle Based on Optimized Artificial Potential Field and Discrete LQR Algorithm by Chaoxia Zhang, Zhihao Chen, Xingjiao Li, Ting Zhao

“…Comparative analysis of visual trajectories pre-optimization and post-optimization highlights improvements. …”
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Optimization Models for Reducing the Air Pollutants Emission in the Production of Insulation Bituminous by Faezeh Borhani, Majid Shafiepour Motlagh, Amir Houshang Ehsani, Yousef Rashidi, Alireza Noorpoor, Saeid Maddah

“…According to the optimization results, the most suitable air temperature and percent excess air were selected to achieve the lowest pollutant emissions. …”
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Optimization of Vehicles Routing Problem using GA For AL-Rasheed municipality, Baghdad, Iraq by zainab talib, Muayed Al-Huseiny

“…As a result, choosing the optimal waste collection policy has a large effect on cost savings. …”
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Application of Multi-Strategy Controlled Rime Algorithm in Path Planning for Delivery Robots by Haokai Lv, Qian Qian, Jiawen Pan, Miao Song, Yong Feng, Yingna Li

“…This research addresses the robot path planning problem, aiming to enhance delivery efficiency and reduce operational costs through systematic improvements to the RIME optimization algorithm. …”
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Comprehensive optimization of active and reactive power scheduling in smart microgrids by accounting for line transmission losses using genetic algorithm by F.Z. Zahraoui, H.E. Chakir, M. Et-taoussi, H Ouadi

“…The grid-connected SMG consists of photovoltaic (PV) cells, BSS, and residential loads. A Genetic Algorithm (GA)-based method is proposed to optimize power management in three scenarios: (i) active power only (AOM), (ii) active and reactive power without line losses (ARM), and (iii) active and reactive power considering line losses (ARLM). …”
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LPBSA: Pre-clinical data analysis using advanced machine learning models for disease prediction by Dana R. Hamad, Tarik A. Rashid

“…The current study introduces an optimization algorithm, Learner Performance-Based Behavior with Simulated Annealing (LPBSA), integrated with Multilayer Perceptron (MLP) as a neural network technique to improve disease prediction accuracy. …”
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Multi-objective optimization and parameter sensitivity study on microreactor nuclear power systems by Ersheng You, Yiyi Li, Jianjun Xu, Dianchuan Xing, Haochun Zhang

“…The PSO algorithm code was used to calculate and compare the benchmark case of MRNPS, and the optimized Pareto optimal solution set was obtained. …”
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A Novel Modified Gorilla Troops Optimizer Algorithm for Interline Power Flow Controller-Based Damping Controller Design by Rajendra Kumar Khadanga, Deepa Das, Sidhartha Panda, Soumya Ranjan Mahapatro, Manoj Kumar Kar, Ahmed Al Mansur, Taha Selim Ustun, Akhtar Kalam

“…Based on the simulation results, the updated strategy that has been suggested is the most effective way to define the mentioned damping controller by considering the percentage improvement in the goal function value.…”
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Spatial feature recognition and layout method based on improved CenterNet and LSTM frameworks by Yuxuan Gu, Fengyu Liu, Xiaodi Yi, Lewei Yang, Yunshu Wang

“…Consequently, this study introduces an advanced spatial feature recognition and layout methodology employing enhanced CenterNet and LSTM (Long Short-Term Memory) frameworks, which is bifurcated into two major components—first, HCenterNet-based feature recognition enhances feature extraction through an attention mechanism and feature fusion technology, refining the identification of small targets within complex background areas; second, a GA-BiLSTM (Genetic Algorithm - Bidirectional LSTM)-based spatial layout model uses a bidirectional LSTM network optimized with a genetic algorithm (GA), aimed at fine-tuning the network parameters to yield more accurate spatial layouts. …”
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A Q-Learning Crested Porcupine Optimizer for Adaptive UAV Path Planning by Jiandong Liu, Yuejun He, Bing Shen, Jing Wang, Penggang Wang, Guoqing Zhang, Xiang Zhuang, Ran Chen, Wei Luo

“…To address these issues, this paper proposes an algorithm named QCPO, which integrates CPO with Q-learning to improve UAV path optimization performance. …”
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Optimizing Traffic Speed Prediction Using a Multi-Objective Genetic Algorithm-Enhanced RNN for Intelligent Transportation Systems by C. Swetha Priya, F. Sagayaraj Francis

“…Additionally, we developed a Multi-Objective Genetic Algorithm (MOGA)-enhanced RNN model to optimize hyperparameters and achieve accurate traffic speed predictions. …”
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Developing a cost-effective tool for choke flow rate prediction in sub-critical oil wells using wellhead data by Zhiwei Xun, Farag M. A. Altalbawy, Prakash Kanjariya, R. Manjunatha, Debasish Shit, M. Nirmala, Ajay Sharma, Sarbeswara Hota, Shirin Shomurotova, Fadhil Faez Sead, Hojjat Abbasi, Mohammad Mahtab Alam

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VCNet: Optimized Deep Learning framework with deep feature extraction and genetic algorithm for multiclass rice crop disease detection by Sanam Salman Kazi, Bhakti Palkar, Dhirendra Mishra

“…It also requires fewer parameters and takes minimum training time. • The major contribution of this study is the design of an optimized, efficient and enhanced deep learning technique for multiclass rice crop disease detection embracing with batch normalization, dropout and genetic optimization algorithm to improve generalization power and restrict the overlearning capability for seen and unseen data. • Proposed VCNet, a shallow model with deep feature extraction, employs VGG16 layers for initial extraction fused with custom CNN architecture to correctly detect the challenging classes of diseases like sheath rot in multiclass classification. • The most significant observation is that VCNet accurately predicts the rice disease for each class of diseases under study whereas the existing powerful models largely misclassified for some classes of diseases in multiclass classification.…”
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Passenger Flow Prediction for Rail Transit Stations Based on an Improved SSA-LSTM Model by Xing Zhao, Chenxi Li, Xueting Zou, Xiwang Du, Ahmed Ismail

“…According to the experimental results, the proposed SSA-LSTM model has a more effective performance than the Support Vector Regression (SVR) method, the eXtreme Gradient Boosting (XGBoost) model, the traditional LSTM model, and the improved LSTM model with the Whale Optimization Algorithm (WOA-LSTM) in the passenger flow prediction. …”
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