Comparative Analysis of Automated Machine Learning for Hyperparameter Optimization and Explainable Artificial Intelligence Models by Muhammad Salman Khan, Tianbo Peng, Hanzlah Akhlaq, Muhammad Adeel Khan

“…For this purpose, Random forest (RF) is selected as the base model and integrated with nine different AutoML frameworks, namely Random search (RS), Grid search (GS), Hyperopt, TPOT, Optuna, GP Minimize, Forest Minimize, GBRT Minimize, and Dummy Minimize. …”
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Application research on classification and integration model of innovation and entrepreneurship education resources based on GNN-PSO algorithm by Yongjian Dong

“…At the same time, it optimizes the classification and integration process with the help of the PSO algorithm's global search advantage. In the experimental link, we constructed an experimental dataset containing 10,000 innovation and entrepreneurship education resources, covering multi-dimensional information such as courses, cases, and teachers. …”
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Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models by Michael Contreras-Ramírez, Jhonathan Sora-Cardenas, Claudia Colorado-Salamanca, Clemencia Ovalle-Bracho, Daniel R. Suárez

“…This study addresses the development of a system based on machine learning algorithms to detect <i>Leishmania</i> spp. parasite in direct smear microscopy images, contributing to the diagnosis of cutaneous leishmaniasis. …”
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Feature selection using a multi-strategy improved parrot optimization algorithm in software defect prediction by Qi Fei, Guisheng Yin, Zhian Sun

“…Comparative experiments demonstrate that BMEPO exhibits stronger competitiveness in feature selection quality and classification performance compared to advanced feature selection algorithms. …”
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A Cross-Stage Focused Small Object Detection Network for Unmanned Aerial Vehicle Assisted Maritime Applications by Gege Ding, Jiayue Liu, Dongsheng Li, Xiaming Fu, Yucheng Zhou, Mingrui Zhang, Wantong Li, Yanjuan Wang, Chunxu Li, Xiongfei Geng

“…The CFSD-UAVNet model was evaluated on the publicly available SeaDronesSee maritime dataset and compared with other cutting-edge algorithms. The experimental results showed that the CFSD-UAVNet model achieved an mAP@50 of 80.1% with only 1.7 M parameters and a computational cost of 10.2 G, marking a 12.1% improvement over YOLOv8 and a 4.6% increase compared to DETR. …”
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Research on three-dimensional path planning of unmanned aerial vehicle based on improved Whale Optimization Algorithm. by HaoCheng Wang, ZeXian Hao, Yu Zhang

“…Firstly, to enhance the search efficiency and fitness accuracy of the Whale Algorithm, the Cuckoo Search and Random Differential Strategy were introduced and compared with the traditional Particle Swarm Optimization algorithm, Whale Algorithm, and Cuckoo Search Algorithm. …”
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Research of UAV 3D path planning based on improved Dwarf mongoose algorithm with multiple strategies by Lixin Mu, Wenhui Liu, Haocheng Wang, Yu Zhang

“…Comparative evaluations on benchmark test functions demonstrate that the improved algorithm (CDMOS) outperforms the original DMO in optimization performance, convergence precision, and overall stability, achieving an average improvement of 53.5% in convergence accuracy and 35.1% in solution stability across 29 benchmark functions. …”
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A modified two-term conjugate gradient-based projection algorithm for constrained nonlinear equations with applications by Yan Xia, Dandan Li, Songhua Wang

“…By combining this with line search approaches and projection techniques, this paper proposes a modified two-term conjugate gradient-based algorithm, specifically for solving constrained nonlinear equations. …”
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A Comparative Analysis of Hyper-Parameter Optimization Methods for Predicting Heart Failure Outcomes by Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada

“…We evaluated three optimization approaches—Grid Search (GS), Random Search (RS), and Bayesian Search (BS)—across three machine learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). …”
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An Enhanced Starfish Optimization Algorithm via Joint Strategy and Its Application in Ultra-Wideband Indoor Positioning by Yu Liu, Maosheng Fu, Zhengyu Liu, Huaiqing Liu, Wei Peng, Ling Li, Yang Yang, Xiancun Zhou, Chaochuan Jia

“…The SFOAL was compared in detail with other algorithms. The experimental results demonstrated that the overall performance of the SFOAL was better than that of other algorithms, and the joint strategy could effectively balance the development and search capabilities to obtain stronger global and local optimization capabilities. …”
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Enhanced crayfish optimization algorithm: Orthogonal refracted opposition-based learning for robotic arm trajectory planning. by Yuefeng Leng, Chunlai Cui, Zhichao Jiang

“…In practical robotic arm trajectory planning experiments, ECOA demonstrated superior performance, reducing costs by 15% compared to the best competing algorithm and 10% over the original COA, with significantly lower variability. …”
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Multi-Station Agricultural Machinery Scheduling Based on Spatiotemporal Clustering and Learnable Multi-Objective Evolutionary Algorithm by Liruizhi Jia, Qinshuo Zhang, Shengquan Liu, Bo Kong, Yuan Liu

“…Multi-objective evolutionary algorithms are sensitive to the initial population quality and local search strategies for path planning, where unreasonable initial solutions or improper local search strategies can affect the diversity of solutions. …”
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Estimation of the Angles of a Robotic Arm with 7-Free Degrees Using an Improved Hybrid ESSA Algorithm by Inayet Hakki Cizmeci, Adem Alpaslan Altun

“…The electro-search algorithm (ESO) encounters challenges arising from its slow convergence rate and propensity to descend into local optima. …”
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Improved fish swarm algorithm based on chaos and its application in abnormal detection of industrial control network by Yaping WU, Hongzhao DONG, Yingying LIN, Jing LIU

“…Artificial fish swarm algorithm as a new type of bionic swarm intelligence optimization algorithm has been successfully used in a variety of optimization problems and practical engineering field,but when faced with the complex optimization problems,especially the multiple extreme value of peak and multimodal function optimization problems,due to the objective function has many local minima,inevitably there are defects such as premature and slow convergence speed.The random and ergodic theory was introduced into the basic artificial fish swarm algorithm,and an improved algorithm was constructed to make artificial fish swarm search avoid possible local extremum.The effectiveness of the algorithm was validated in the simulation application in industrial control network anomaly detection.Compared with the basic artificial fish school algorithm,the chaos improved artificial fish swarm algorithm can effectively avoid the long-term search of the algorithm near the local extreme value.The algorithm has better performance in terms of global convergence and the search efficiency is more prominent.…”
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Optimizing Artificial Neural Network Learning Using Improved Reinforcement Learning in Artificial Bee Colony Algorithm by Taninnuch Lamjiak, Booncharoen Sirinaovakul, Siriwan Kornthongnimit, Jumpol Polvichai, Aysha Sohail

“…Artificial neural networks (ANNs) are widely used machine learning techniques with applications in various fields. Heuristic search optimization methods are typically used to minimize the loss function in ANNs. …”
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Comparative study on inversion of the unsaturated hydraulic parameters using optimization and Bayesian estimation methods by KE Fengqiao, MAN Jun, ZENG Lingzao, WU Laosheng

“…With the measurements obtained from a one dimensional sand column infiltration experiment, the soil hydraulic parameters were estimated by two approaches, i. e., the MCMC algorithm and the Levenberg-Marquardt (LM) nonlinear optimization algorithm. …”
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Two-stage iterative data transmission scheduling algorithm for satellite Internet integrating communication and remote sensing by LI Hai, LI Yongjun, ZHANG Taijiang, LI Yu, ZHAO Shanghong, WANG Jie, HAN Yue

“…In the two-stage iterative optimization process, the Metropolis criterion was introduced to improve the global search capability. Simulation results demonstrate that the proposed TSIDTS algorithm significantly improves data transmission profits compared with the same type data transmission scheduling algorithms, and confirm that the inter-satellite communication link can significantly improve the timeliness of remote sensing data downloading.…”
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Three-Dimensional Path Planning for Unmanned Aerial Vehicles Based on Hybrid Multi-Strategy Dung Beetle Optimization Algorithm by Hongmei Fei, Ruru Liu, Leilei Dong, Zhaohui Du, Xuening Liu, Tao Luo, Jie Zhou

“…First, a new Latin hypercube sampling strategy is introduced to significantly enhance the population diversity and improve the global search capabilities. Furthermore, an innovative golden sine strategy is proposed to greatly enhance the algorithm’s robustness. …”
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Adaptive chaotic gaussian lens snake optimization algorithm for improved cotton field sensor coverage and utilization by Chenglong Ban, Xiang Liu, Min Tian, Zhonghua Lu, Ruoxue Xiang, Jie Zhou

“…Additionally, the algorithm employs an improved Gaussian operator and a lens reflection mechanism to expand the search space, thereby enhancing global search performance. …”
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Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems by Lhassane Idoumghar, Mahmoud Melkemi, René Schott, Maha Idrissi Aouad

“…The results are compared with these obtained by existing hybrid PSO-SA algorithms. …”
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