Cycle-based formulations in Distance Geometry by Liberti, Leo, Iommazzo, Gabriele, Lavor, Carlile, Maculan, Nelson

“…The problem is often modelled as a mathematical programming formulation involving decision variables that determine the position of the vertices in the given Euclidean space. Solution algorithms are generally constructed using local or global nonlinear optimization techniques. …”
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Adaptive Cut Selection in Mixed-Integer Linear Programming by Turner, Mark, Koch, Thorsten, Serrano, Felipe, Winkler, Michael

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Efficient persistence landscape generation by Taylor Henderson, Robert Simon

“…Our algorithm can determine in optimal O ( n * log ( n ) ) if a given birth-death pair appears in the top- k landscapes. …”
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An Improved Crop Disease Identification Method Based on Lightweight Convolutional Neural Network by Tingzhong Wang, Honghao Xu, Yudong Hai, Yutian Cui, Ziyuan Chen

“…In order to improve the training learning rate, Adam optimizer combining momentum algorithm and RMSprop algorithm is used to dynamically adjust the learning rate; the combination of the two algorithms makes the loss function converge to the lowest point faster. …”
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An Improved Differential Evolution Method Based on the Dynamic Search Strategy to Solve Dynamic Economic Dispatch Problem with Valve-Point Effects by Guangyu Chen, Xiaoqun Ding

“…DE is the main optimizer in the method proposed. While chaotic sequences are applied to obtain the dynamic parameter settings in DE, dynamic search strategy which consists of two steps, global search strategy and local search strategy, is used to improve algorithm efficiency. …”
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Pengembangan Deep Learning untuk Sistem Deteksi Dini Komplikasi Kaki Diabetik Menggunakan Citra Termogram by Medycha Emhandyksa, Indah Soesanti, Rina Susilowati

“…In this study, four deep convolutional neural network models were designed with Occam's razor principle through hyperparameter settings on the algorithm structure aspect in the form of number of layers and optimization aspect in the form of optimizer type. …”
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A smarter approach to liquefaction risk: harnessing dynamic cone penetration test data and machine learning for safer infrastructure by Shubhendu Vikram Singh, Sufyan Ghani

“…ML models, including Support Vector Machine (SVM) optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Firefly Algorithm (FA), were employed to predict the e/qd ratio using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, and penetration rate. …”
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Prediction model of middle school student performance based on MBSO and MDBO-BP-Adaboost method by Rencheng Fang, Tao Zhou, Baohua Yu, Zhigang Li, Long Ma, Tao Luo, Yongcai Zhang, Xinqi Liu

“…Firstly, the model incorporates the good point set initialization, triangle wandering strategy and adaptive t-distribution strategy to obtain the Modified Dung Beetle Optimization Algorithm (MDBO), secondly, it uses MDBO to optimize the weights and thresholds of the BP neural network, and lastly, the optimized BP neural network is used as a weak learner for Adaboost. …”
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Energy Efficiency Maximization for Device-to-Device Communication Underlaying Cellular Networks on Multiple Bands by Yuan Zhang, Yang Yang, Linglong Dai

“…However, most of existing works only optimize the EE in the single-cell scenario, while little attention is paid to maximizing the EE of the whole cellular network underlaid with D2D communication with randomly distributed users on multiple bands. …”
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Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing by Ruijin Sun, Yao Wen, Nan Cheng, Wei Wang, Rong Chai, Yilong Hui

“…Although this non-convex problem can be handled by the alternating minimization (AM) algorithm that alternatively minimizes the divided four sub-problems, it leads to high computational complexity and local optimal solution. …”
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A Decomposition-Integration Framework of Carbon Price Forecasting Based on Econometrics and Machine Learning Methods by Zhehao Huang, Benhuan Nie, Yuqiao Lan, Changhong Zhang

“…Empirical evidence demonstrates that this framework not only accurately captures the diverse characteristics of different data components but also significantly outperforms traditional benchmark models in predictive accuracy. By optimizing the GRU model with the grey wolf optimizer (GWO) algorithm, the framework enhances both prediction stability and adaptability, while the nonlinear integration approach effectively mitigates error accumulation. …”
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Microgrid system for electric vehicle charging stations integrated with renewable energy sources using a hybrid DOA–SBNN approach by Kommoju Naga Durga Veera Sai Eswar, M. Arun Noyal Doss, Mohammad Shorfuzzaman, Ali Elrashidi

“…The proposed method outperforms all current techniques, including the Multi swarm Optimization (MSO), the Multi-Objective Gray Wolf Optimizer (MOGWO), and the Modified Multi-objective Salp Swarm Optimization algorithm (MMOSSA). …”
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Retrospective Illumination Correction of Retinal Images by Libor Kubecka, Jiri Jan, Radim Kolar

“…Among the tested optimizers, the gradient-based optimizer with varying step has shown to have the fastest convergence while providing the best precision. …”
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DSGD++: Reducing Uncertainty and Training Time in the DSGD Classifier through a Mass Assignment Function Initialization Technique by Aik Tarkhanyan, Ashot Harutyunyan

“…We present a method for the Dempster-Shafer Gradient Descent (DSGD) algorithm that significantly reduces training time—by a factor of 1.6—and also reduces the uncertainty of each rule (a condition on features leading to a class label) by a factor of 2.1, while preserving accuracy comparable to other statistical classification techniques. …”
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ARIMA-Kriging and GWO-BiLSTM Multi-Model Coupling in Greenhouse Temperature Prediction by Wei Zhou, Shuo Liu, Junxian Guo, Na Liu, Zhenglin Li, Chang Xie

“…This study introduces an ARIMA-Kriging spatiotemporal coupling model, which combines temperature time-series data with sensor spatial coordinates to accurately determine minimum temperatures in greenhouses while reducing hardware costs. Utilizing the high-quality data processed by this model, this study proposes and constructs a novel Grey Wolf Optimizer and Bidirectional Long Short-Term Memory (GWO-BiLSTM) temperature prediction framework, which combines a Grey Wolf Optimizer (GWO)-enhanced algorithm with a Bidirectional Long Short-Term Memory (BiLSTM) network. …”
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Unsupervised fake news detection on social media using hybrid Gaussian Mixture Model. by Sajida Perveen, Muhammad Shahbaz, Sami S Albouq, Khlood Shinan, Hanan E Alhazmi, Fatmah Alanazi, M Usman Ashraf, Rehan Ashraf

“…In particular, it also proposes a novel hybrid method that leverages the Gaussian Mixture Model (GMM) in conjunction with the Group Counseling Optimizer (GCO), a metaheuristic optimization algorithm, to identify the optimal number of clusters for the detection of fake news. …”
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Inverse design of perimeter-controlled InAs-assisted metasurface for two-dimensional dynamic beam steering by Sabri Raana, Mosallaei Hossein

“…The multi-objective genetic algorithm (GA) for optimizing user-defined metrics toward shaping desired far-zone radiation pattern is implemented. …”
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UAV-Enabled Inspection System With No-Fly Zones: DRL-Based Joint Mobile Nest Scheduling and UAV Trajectory Design by Jin Dai, Yunfei Gao, Chao Cai, Wei Xiong, Manjia Liu

“…This paper investigates a UAV-enabled inspection system in an urban environment with no-fly zones (NFZs), where the UAV flies to inspection points to capture images while constrained by limited onboard energy. The aim of this paper is to minimize the whole inspection time via joint optimization of the mobile nest’s scheduling and UAV trajectory while satisfying constraints related to energy maximization and avoiding NFZs. …”
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Implementation of Machine Vision Methods for Cattle Detection and Activity Monitoring by Roman Bumbálek, Tomáš Zoubek, Jean de Dieu Marcel Ufitikirezi, Sandra Nicole Umurungi, Radim Stehlík, Zbyněk Havelka, Radim Kuneš, Petr Bartoš

“…The goal of this research was to implement machine vision algorithms in a cattle stable to detect cattle in stalls and determine their activities. …”
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What Strategy Central Nervous System Uses to Perform a Movement Balanced? Biomechatronical Simulation of Human Lifting by Ali Leylavi Shoushtari

“…To solve the kinematic redundancy in previous studies it is hypothesize that CNS functions as an optimizer, such of that are the task-based algorithms which search to find optimal solution for each specific task. …”
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