EPRSA: interference resource scheduling algorithms for air-ground communication networks by Wenpeng Wu, Zhenhua Wei, Haiyang You, Xiaoqing Li, Jianwei Zhan, Zhaoguang Zhang

“…Strategies such as cycle and initial quantity selection, elastic initialization, double tabu power scheduling, and jammer conversion are designed to improve the scheduling ability of the Elastic Parallel Random Search Algorithm (EPRSA) in terms of system operation duration and interference cost. …”
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Optimization of the Weight Processing Algorithm in Multichannel Doppler Filtering by V. I. Koshelev, Ngoc Hieu Trinh

“…Separate optimization of weighting processing for each frequency channel can significantly improve the average efficiency characteristics of a multichannel Doppler filter and eliminate all the shortcomings of the classical and modified FFT algorithms when processing non-equidistant pulse sequences. …”
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Reliability growth model of quantum direct current electricity meter software based on optimization network by TIAN Teng, QIU Rujia, ZHAO Long, GENG Jiaqi, WANG Enhui, SUN Yu

“…This improves the modeling efficiency by 18 times and significantly improves global optimization ability of the back propagation neural network. …”
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Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications by João P. Castanheira, Beltran N. Arribas, Rui Melicio, Paulo Gordo, André R. R. Silva

“…This research employs a series of numerical simulations using methods such as Genetic Algorithms, Cultural Algorithms, and Artificial Immune Systems, with an emphasis on parameter tuning to optimize the reduced thermal model correlation. …”
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Federated learning optimization algorithm based on incentive mechanism by Youliang TIAN, Shihong WU, Ta LI, Lindong WANG, Hua ZHOU

“…Federated learning optimization algorithm based on incentive mechanism was proposed to address the issues of multiple iterations, long training time and low efficiency in the training process of federated learning.Firstly, the reputation value related to time and model loss was designed.Based on the reputation value, an incentive mechanism was designed to encourage clients with high-quality data to join the training.Secondly, the auction mechanism was designed based on the auction theory.By auctioning local training tasks to the fog node, the client entrusted the high-performance fog node to train local data, so as to improve the efficiency of local training and solve the problem of performance imbalance between clients.Finally, the global gradient aggregation strategy was designed to increase the weight of high-precision local gradient in the global gradient and eliminate malicious clients, so as to reduce the number of model training.…”
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Federated learning optimization algorithm based on incentive mechanism by Youliang TIAN, Shihong WU, Ta LI, Lindong WANG, Hua ZHOU

“…Federated learning optimization algorithm based on incentive mechanism was proposed to address the issues of multiple iterations, long training time and low efficiency in the training process of federated learning.Firstly, the reputation value related to time and model loss was designed.Based on the reputation value, an incentive mechanism was designed to encourage clients with high-quality data to join the training.Secondly, the auction mechanism was designed based on the auction theory.By auctioning local training tasks to the fog node, the client entrusted the high-performance fog node to train local data, so as to improve the efficiency of local training and solve the problem of performance imbalance between clients.Finally, the global gradient aggregation strategy was designed to increase the weight of high-precision local gradient in the global gradient and eliminate malicious clients, so as to reduce the number of model training.…”
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USING REINFORCEMENT LEARNING ALGORITHMS FOR UAV FLIGHT OPTIMIZATION by O. Dutsiak, V. Yuzevych

“…The study of the results of the functionality of the proposed algorithm was carried out in the environment of three-dimensional modeling. …”
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Optimal Placement of Phasor Measurement Unit in Electrical Grid Using Dingo Optimization Algorithm by ARIYO Funso Kehinde, AYANLADE Samson Oladayo, JIMOH Abdulrasaq, ADEBAYO Moses Taiwo

“…The study utilizes the Dingo Optimization Algorithm, a metaheuristic inspired by nature, to identify the best PMU placement. …”
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Adaptive multilevel attention deeplabv3+ with heuristic based frame work for semantic segmentation of aerial images using improved golden jackal optimization algorithm by Anilkumar P, Venugopal P, Satheesh Kumar S, Jagannadha Naidu K

“…To addressing the issue in deeplab series, an adaptive multi-level attention based deeplabv3+ (AMLA-Deeplabv3+) with improved golden jackal optimization algorithm is implemented in this paper. …”
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Multiscale feature tuned trans-DeepLabV3+ based semantic segmentation of aerial images using improved red piranha optimization algorithm by P. Anilkumar, K. Lokesh, A. Naveen Kumar, D. John Pradeep, Y. V. Pavan Kumar, Rammohan Mallipeddi

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Adaptive predator prey algorithm for many objective optimization by Nikunj Mashru, Kanak Kalita, Lenka Čepová, Pinank Patel, Arpita, Pradeep Jangir

“…This paper presents the Many-Objective Marine Predator Algorithm (MaOMPA), an adaptation of the Marine Predators Algorithm (MPA) specifically enhanced for many-objective optimization tasks. …”
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Comparative study of whale optimization algorithm and flower pollination algorithm to solve workers assignment problem by Huthaifa Al-Khazraji

“…The WAP is to find the best assignment of workers to training courses such that the total training cost is minimized. Two metaheuristic optimizations named Whale Optimization Algorithm (WOA) and Flower Pollination Algorithm (FPA) are utilized to final the optimal solution that reduces the total cost. …”
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Revocation encryption scheme based on domestic cryptographic algorithm SM9 by XIE Zhenjie, ZHANG Wanli, ZHANG Yao, ZHAO Fangfang, LIU Shengli

“…To address the limitations of lengthy system public keys and inefficient decryption in existing identity-based revocation encryption schemes, an optimized revocation encryption scheme was proposed based on SM9, China’s independently developed identity-based cryptographic algorithm. …”
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A Hybrid Algorithm with a Data Augmentation Method to Enhance the Performance of the Zero-Inflated Bernoulli Model by Chih-Jen Su, I-Fei Chen, Tzong-Ru Tsai, Yuhlong Lio

“…This zero-inflated structure significantly contributes to data imbalance. To improve the ZIBer model’s ability to accurately identify minority classes, we explore the use of momentum and Nesterov’s gradient descent methods, particle swarm optimization, and a novel hybrid algorithm combining particle swarm optimization with Nesterov’s accelerated gradient techniques. …”
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Multi-clustering algorithm based on improved tensor chain decomposition by ZHANG Hongjun, ZHANG Zeyu, ZHANG Yingjiao, YE Hao, PAN Gaojun

“…The innovations were mainly reflected in two aspects: firstly, a new tensor decomposition framework was proposed, which effectively reduced the storage cost and improved the computational efficiency by optimizing the objective function; secondly, the improved tensor decomposition technique was applied to three main multi-clustering algorithms, including self-weighted multi-view clustering (SwMC), latent multi-view subspace clustering (LMSC), and multi-view subspace clustering with intactness-aware similarity (MSC IAS), which significantly improved the accuracy and efficiency of clustering. …”
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A new adaptive grey prediction model and its application by Jianming Jiang, Ming Zhang, Zhongyong Huang

“…Specifically, the Marine Predators Optimization algorithm is introduced to facilitate the model’s solution process. …”
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Optimization model and heuristic solution method for multi-channel cooperative sensing in cognitive radio networks by Wei YANG, Dong-song BAN, Huan-zhong LI, Wen-hua DOU

“…An optimization model under the scenario where multi-channels are cooperatively sensed and used by multi-secondary users (SU) was proposed.The model aims to maximize the system throughput and optimizes the parameters including the sensing time and the weight coefficient of the sampling result of each SU for each channel,meanwhile the false access probability for each channel must not violate the given constraints.To solve this non-linear optimization model,a sequential parameters optimization method(SPO)was proposed.The method begins with deriving the lower bound of the objective function of the optimization model.Then it maximizes this lower bound by optimizing the weight coefficients through solving a series of sub-optimal problems using Lagrange method,and finally finding an optimized sensing time parameter by the golden search algorithm.Extensive experiments by simulations demonstrate the effectiveness of the proposed method and the advantage of the proposed model on improving the system throughput.…”
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Landslide Displacement Prediction Model Based on Optimal Decomposition and Deep Attention Mechanism by Shuai Ren, Kamarul Hawari Ghazali, Yuanfa Ji, Samra Urooj Khan

“…Experimental results demonstrate that the proposed model significantly improves predictive performance, reducing the Root Mean Square Error (RMSE) by 60% compared to the traditional XGBoost model and by 33% compared to the Empirical Mode Decomposition-BiLSTM (EMD-BiLSTM) model. …”
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Distributed Optimization Strategy for Voltage Regulation in PV-Integrated Power Systems with Limited Sensor Deployment by Xun Lu, Junlei Liu, Xinmiao Liu, Jun Liu, Lingxue Lin

“…The methodology integrates an adaptive step size algorithm within a dynamic projected primal–dual distributed optimization framework, eliminating manual parameter tuning requirements while ensuring theoretical convergence guarantees through Lyapunov stability analysis. …”
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Improved SOM algorithm for damage characterization based on visual sensing by Hongtao Zhu, Shuyun Guo

“…Additionally, employing stochastic gradient descent as an optimization algorithm enhances the model training efficiency. …”
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