Salp Navigation and Competitive based Parrot Optimizer (SNCPO) for efficient extreme learning machine training and global numerical optimization by Oluwatayomi Rereloluwa Adegboye, Afi Kekeli Feda, Ghanshyam G. Tejani, Aseel Smerat, Pankaj Kumar, Ephraim Bonah Agyekum

“…To validate the efficacy of SNCPO, rigorous experimental evaluations were conducted on CEC2015 and CEC2020 benchmark functions, four engineering design optimization problems, and Extreme Learning Machine (ELM) training tasks across 14 datasets. …”
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Enhanced Dung Beetle Optimizer-Optimized KELM for Pile Bearing Capacity Prediction by Bohang Chen, Mingwei Hai, Gaojian Di, Bin Zhou, Qi Zhang, Miao Wang, Yanxiu Guo

“…Initially, experimental data on pile bearing capacity was gathered from the existing literature and subsequently normalized to facilitate effective integration into the model training process. A detailed introduction of the multi-strategy improved beetle optimization algorithm (IDBO) is provided, with its superior performance validated through 23 benchmark functions. …”
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Cons-training tensor networks: Embedding and optimization over discrete linear constraints by Javier Lopez-Piqueres, Jing Chen

“…We further develop a novel canonical form for these new MPS, which allow for the merging and factorization of tensor blocks according to quantum region fusion rules and permit optimal truncation schemes. Utilizing this canonical form, we apply an unsupervised training strategy to optimize arbitrary objective functions subject to discrete linear constraints. …”
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Leveraging Attribute Interaction and Self-Training for Graph Alignment via Optimal Transport by Songyang Chen, Youfang Lin, Ziyuan Zeng, Mengyang Xue

“…However, it remains largely unexplored under the OT framework to fully utilize both structure and attribute information. We propose an Optimal Transport-based Graph Alignment method with Attribute Interaction and Self-Training (<b>PORTRAIT</b>), with the following two contributions. …”
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Multi-objective optimization of auxiliary wireless power supply system for maglev trains by CHENG Long, DONG Kan, WANG Shuo

“…A global multi-objective optimization design strategy was introduced based on the concept ofPareto optimal solutions. …”
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Optimization Method of High-Speed Train Composite Material Workshop Planning and Scheduling by Yanliang Jie, Lei Hao, Shujun Yan, Xueli Zhang

“…This paper mainly studies the high-speed train composite workshop planning and scheduling optimization method. …”
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Optimizing strategy for training of home blood pressure monitoring in hypertension patients (优化家庭血压测量技术培训在高血压患者健康教育中的应用) by YAO Zhiping (姚志萍)

“…Objective To investigate the effect of optimized strategy for training of home blood pressure monitoring in hypertension patients. …”
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Research on False Alarm Rate Reducing Strategies for Subway Train Fire Alarm System by ZHANG Qian, XU Shaohong, XUE Hongquan

“…[Objective] Given the high false alarm rate in subway train fire alarm systems, it is necessary to study strategies of effectively reducing the false alarm rate, so as to minimize operational disruptions and resource waste caused by false alarms, enhancing subway operation safety and passengers′ travel experience. …”
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Development and Training Strategy of Badminton Action Recognition System Under the Background of Artificial Intelligence by Hongjun Ma, Fan Zhang, Ni Liang

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Challenges and Strategies for Expanding Enterprise-Based Training to Develop Skills for the ICT Industry in the Philippines by Jooyoung Voeller

“… Purpose: Since 2000, the Philippines has experienced significant growth domestic product (GDP) growth, particularly in the Information Technology Business Process Outsourcing (IT-BPO) sector, highlighting the need for skill development through Technical Vocational Education and Training (TVET). Although enterprise-based training (EBT) is seen as the optimal strategy for supporting transition to a technology-driven economy, it represents less than 4% of TVET offerings. …”
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A DQN-based approach for energy-efficient train driving control by Shuai SU, Qingyang ZHU, Qinglai WEI, Tao TANG, Jiateng YIN

“…The energy consumption in railway system is growing rapidly due to the expanding scale of the railway network and decreased operational headway.Hence, it is of great significant to apply the energy-efficient operation of the vehicles to cut down the energy cost of the railway system.A method for solving the energy-efficient train driving control based on deep Q-network (DQN) approach was proposed.Firstly, the traditional energy-efficient train driving control problem was presented and its inverse problem was formulated, i.e., distributing the least energy consumption units to achieve the scheduled trip time.Moreover, the problem was reformulated as a Markov decision process (MDP) and a DQN-based approach for energy-efficient train driving control was proposed.A DQN was built to approximate the action value function which determines the optimal energy distribution policy and further obtain the optimal driving strategy.Finally, a numerical experiment based on the real-world operational data was proposed to verify the effectiveness of the proposed method and analyze the performance of the proposed method.The driving data of the trains is applied to improve the driving strategy via the proposed method in the paper which reduces the traction energy consumption.It is of significance for the future development of Chinese intelligent urban railway system.…”
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SimpleScale: Simplifying the Training of an LLM Model Using 1024 GPUs by Tianfa Li, Jingshan Pan, Siwei Ma, Aleksandr Raikov, Alexander Arkhipov

“…This paper develops SimpleScale for building LLMs based on FSDP and Slurm, which is a simple and efficient training system that includes the training agent, the efficient parallel strategy, the optimal step of checkpoint, and so on. …”
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Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout by Mehdi Ghayoumi

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The physiology and psychology of negative splits: insights into optimal marathon pacing strategies by Gerasimos V. Grivas

“…Among the available pacing approaches, the negative split running the second half faster than the first has emerged as a potentially optimal strategy for endurance athletes. This mini-review explores the physiological mechanisms and psychological factors underpinning the effectiveness of negative splits in marathon running. …”
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Optimization Strategy of Building Energy System Based on Deep Reinforcement Learning by Wenzhe SHI, Bingjie LI, Peipei YOU, Ling ZHANG

“…Aiming at the load uncertainty on the demand side of the building energy system and the randomness of renewable energy on the supply side, a building energy system management optimization strategy is proposed based on deep reinforcement learning. …”
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Optimization Strategies for Urban Waterlogging Warning in Complex Environments: Based on Particle Swarm Optimization and Deep Neural Networks by Xiande Hu, Fenfei Gu

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Data-driven power marketing strategy optimization and customer loyalty promotion by Bo Chen, Wei Cui

“…In the power market, companies face challenges in enhancing customer loyalty and optimizing strategies. This study employs the LSTM network model, trained on combined time-series power consumption, customer interaction scores and market response rates data. …”
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Configural processing as an optimized strategy for robust object recognition in neural networks by Hojin Jang, Pawan Sinha, Xavier Boix

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Optimizing the social media promotion strategy to improve the branding of Putak Tourism Villages by Dwi Martiyanti, Muhammad Heldir, Pandu Aditya, Ryan Wiranata, Kenny Mandala Chandra Tjiang, Richard

“…This community service aims to optimize promotional strategies through social media to improve the branding of Putak Tourism Village. …”
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Discrete Optimization on Train Rescheduling on Single-Track Railway: Clustering Hierarchy and Heuristic Search by Zhengkun Zhang, Changfeng Zhu, Wenhu Ma

“…After the construction and analysis of optimization models to discrete dynamic system, a two-stage heuristic search strategy is developed, by using clustering hierarchy theory and stochastic search strategy, to obtain train departure time and arrival time before each state transition of the system. …”
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