A solution to the Single-School school bus routing problem considering accessibility and economy by Zizhan Jiang, Jiying Wang, Siyuan Huang, Hua Daniel Xu

“…In the third stage, Dijkstra’s algorithm is combined with GIS to optimize the bus routes, with a focus on accessibility and economy.Experimental results demonstrate that the THGO method can efficiently find the global optimal solution while satisfying constraints such as time windows, bus capacity, and road directionality. …”
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An Efficient Resource Allocation Algorithm for Task Offloading in the Internet of Vehicles by Ahmad Salehi, Sadoon Azizi

“…Simulation results demonstrate that the proposed algorithm reduces memory costs and penalties from rejected tasks, while improving overall system profit. …”
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Solving Integer Ambiguity Based on an Improved Ant Lion Algorithm by Wuzheng Guo, Yuanfa Ji, Xiyan Sun, Xizi Jia

“…First, the performance of the SAALO algorithm was evaluated by comparing its solving speed and success rate with those of the Ant Lion Optimization Algorithm (ALO), the LAMBDA algorithm and the MLAMBDA algorithm. …”
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Finding Random Integer Ideal Flow Network Signature Algorithms by Kardi Teknomo, Erna Budhiarti Nababan, Indriati Njoto Bisono, Resmana Lim

“…We introduce two pseudocode algorithms that uphold flow conservation while maintaining network irreducibility, ensuring autonomy through strong connectivity. …”
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Underwater Object Detection Algorithm Based on an Improved YOLOv8 by Fubin Zhang, Weiye Cao, Jian Gao, Shubing Liu, Chenyang Li, Kun Song, Hongwei Wang

“…Next, the incorporation of a Partial Self-Attention (PSA) module at the end of the backbone network enhances model efficiency and optimizes the utilization of computational resources while maintaining high performance. …”
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Weed detection on embedded systems using computer vision algorithms by D. Shadrin, S. Illarionova, R. Kasatov, M. Akimenkova, G. Rudensky, E. Erhan

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Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…To address the multi-factor coupling, temporal dependency, and nonlinear complexity characteristics of concrete dam deformation, this paper proposed a hybrid prediction model integrating attention mechanisms, bidirectional long short-term memory (BiLSTM) networks, and sparrow search algorithm (SSA). The model comprehensively matched the multiple requirements of information weighting, temporal dependency modeling, and model optimization in concrete dam deformation prediction, forming a synergistic enhancement effect among methods.The attention mechanism operates on both feature and temporal dimensions to comprehensively enhance the model's focus on critical information. …”
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Opposition-Based White Shark Optimizer for Optimizing Modified EfficientNetV2 in Road Crack Classification by Mohammed Al-Shalabi, Mohammed A. Mahdi, Malik Braik, Mohammed Azmi Al-Betar, Shahanawaj Ahamad, Sawsan A. Saad

“…Preprocessing techniques are first applied to eliminate noise and enhance image quality. The OWSO algorithm is then integrated to optimize the classification performance of MEfficientNetV2, while PCA accelerates the learning process by retaining critical features in the thresholds of the varying components. …”
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Constrained Bayesian Optimization: A Review by Sasan Amini, Inneke Vannieuwenhuyse, Alejandro Morales-Hernandez

“…Though it has been widely used to solve various optimization tasks, most of the literature has focused on unconstrained settings, while many real-world problems are characterized by constraints. …”
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GOAL ORIENTED SMART PORTFOLIO OPTIMIZATION by Ayush Deshpande, Rohan Gore, Sankirti Shiravale, Shubham Chougule, Samarth Bahirgonde

“…Portfolio optimization is a critical aspect of investment strategy, which aims to maximize returns while minimizing risk. …”
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A Robust Multi-Objective Evolutionary Framework for Artificial Island Construction Scheduling Under Dynamic Constraints by Tianju Zheng, Liping Sun, Mingwei Li, Guangyao Yuan, Shuqi Li

“…To effectively address the complexities of this model, we develop and employ the Multi-Objective Adaptive Cooperative Evolutionary Marine Genetic Algorithm (MACEMGA). MACEMGA combines cooperative coevolution, adaptive dynamic weighting, dynamic penalty functions, and advanced genetic operators to navigate the solution space efficiently and identify Pareto optimal schedules. …”
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A Comprehensive System to Support Decision Making in Highly Complex Project Portfolio Situations by Efrain Solares, Eduardo Fernandez, Carlos A. Coello Coello, Xochitl Segura Lozano, Reimundo Moreno-Cepeda, Raymundo Diaz

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Short term traffic flow prediction and timing optimization at signalized intersections based on SG-LSTM and particle swarm optimization by Lei Yang, Ruijun Guo

“…The optimized signal timing scheme is derived through the implementation of the particle swarm optimization algorithm in Matlab. …”
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Optimizing ML models for cybercrime detection: balancing performance, energy consumption, and carbon footprint through multi-objective optimization by Romil Rawat

“…Abstract This study aims to enhance computational performance while minimizing environmental impact in AI (Artificial Intelligence) and ML (Machine Learning) applications, especially in cybersecurity, by developing energy-efficient models using a multi-objective optimization approach. …”
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Sensitivity Analysis Based on E-TOPSIS Combined with MORIME-Based Multi-Objective Optimization for Sprayer Frame Design Optimization by Shaohao Zhou, Qi Chen, Lihua Zhang

“…Finally, the multi-objective lightweight design of the selected components was performed based on the MORIME algorithm. After optimization, the stress increased by 12.01% and 1.52% under two operating conditions, while deformation increased by 0.647 mm and 0.607 mm, and the frame mass was reduced by 22.754 kg, a decrease of 12.8%. …”
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Dual-target WOA spectrum sharing algorithm based on Stackelberg game by Li ZHANG, Tian LIAO, Yejun HE

“…In order to solve the complex spectrum allocation problem,a dual-target whale optimization algorithm (WOA) with strong parallel computing capabilities was introduced,and a Stackelberg game model was proposed that could effectively reflect the actual spectrum requirements,and a dual-target WOA optimized distributed antenna system (DAS) spectrum sharing scheduling algorithm was designed.Simulation results show that performance comparison is performed from multiple indicators such as optimal pricing and user benefits.The proposed algorithm has a good spectrum sharing allocation effect,can achieve fair and effective spectrum allocation,and provides an important reference for the future communication network spectrum sharing mode.…”
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Evolutionary search algorithm for learning activation function of an artificial neural network by Yurshin Viacheslav

“…The proposed method aims to enhance the efficiency of the search process for optimal activation functions. Our algorithm employs genetic programming to evolve the general form of activation functions, while gradient descent optimizes their parameters during network training. …”
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Generation of Sparse Antennas and Scatterers Based on Optimal Current Grid Approximation by Tuan Phuong Dang, Manh Tuan Nguyen, Adnan F. Alhaj Hasan, Talgat R. Gazizov

“…To solve this task, we present a novel algorithm based on the optimal current grid approximation for generating sparse scattering structures and evaluating their effectiveness. …”
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Use of schedule theory algorithms for task planning and time management tasks by М. Шматко, О. Жураковська

“…At the moment, there are many algorithms for solving such tasks, but the algorithms must be customized to specific conditions, while ensuring the quality of the task and performance. …”
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Image forgery detection algorithm based on U-shaped detection network by Zhuzhu WANG

“…Aiming at the defects of traditional image tampering detection algorithm relying on single image attribute,low applicability and current high time-complexity detection algorithm based on deep learning,an U-shaped detection network image forgery detection algorithm was proposed.Firstly,the multi-stage feature information in the image by using the continuous convolution layers and the max-pooling layers was extracted by U-shaped detection network,and then the obtained feature information to the resolution of the input image through the upsampling operation was restored.At the same time,in order to ensure higher detection accuracy while extracting high-level semantic information of the image,the output features of each stage in U-shaped detection network would be merged with the corresponding output features through the upsampling layer.Further the hidden feature information between tampered and un-tampered regions in the image upon the characteristics of the general network was explored by U-shaped detection network,which could be realized quickly by using its end-to-end network structure and extracting the attributes of strong correlation information among image contexts that could ensure high-precision detection results.Finally,the conditional random field was used to optimize the output of the U-shaped detection network to obtain a more exact detection results.The experimental results show that the proposed algorithm outperforms those traditional forgery detection algorithms based on single image attribute and the current deep learning-based detection algorithm,and has good robustness.…”
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