Augmented robustness in home demand prediction: Integrating statistical loss function with enhanced cross-validation in machine learning hyperparameter optimisation by Banafshe Parizad, Ali Jamali, Hamid Khayyam

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The choice of the optimal braking force on the wheelset, taking into account the imper-fection of antiskid devices by I. A. Zharov, S. B. Kurtsev, A. A. Makas

“…This allows to set the task of choosing the optimal braking force on the wheel pair by the criterion of minimizing losses due to the increase in braking distances.To formulate the optimization problem, it is necessary to relate the gain from the reduction of the braking distances with good adhesion and the loss from its increase with poor adhesion due to the imperfect operation of the antiskid devices. …”
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Long Term Optimal DG Placement Considering Transmission System Reliability and Load Uncertainty by Ali Badri, Mahdy Norouzi

“…To get more accurate results the model considers both DG benefits and costs and the objective function is based on DG profit maximization. Benefits of using DG consist of loss reduction revenue, reducing in costumers' interruption costs, power purchase saving as well as green house gas and fossil fuel reductions. …”
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Optimization of resource allocations in 5G mobile network using Active Reward Learning by Taghi Shahgholi, Keyhan Khamforoosh, Amir Sheikhahmadi, Sadoon Azizi

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Autonomous deployment and energy efficiency optimization strategy of UAV based on deep reinforcement learning by Yi ZHOU, Xiaoyong MA, Fuxiao GAO, Wei LI, Nan CHENG, Ning LU

“…Utilizing a UAV to build aerial mobile small cell can provide more flexible and efficient access services for ground terminal users.Constrained by the coverage and limited energy of the UAV,it is necessary to study how to build a fast,efficient and energy-saving air-ground collaborative network.To deal with complex dynamic scenarios,the UAV needs to deploy an optimal coverage position,and meanwhile reduce both path loss and energy consumption in the deployment process.Based on the deep reinforcement learning,a strategy of autonomous UAV deployment and efficiency optimization was proposed.The coverage state set of UAV was established,and the energy efficiency was used as a reward function.Depth neural network and Q-learning were used to guide UAV to make autonomous decision and deploy the optimal position.The simulation results show that the deployment time of the proposed method can be effectively reduced by 60%,while the energy consumption can be reduced by 10%~20%.…”
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Deep Learning Strategies for Intraday Optimal Carbon Options Trading with Price Impact Considerations by Qianhui Lai, Qiang Yang

“…Since trading a large-size order in the market will influence the price, the trader needs to design a trading strategy to maximize the profit and loss (PnL). We propose a deep learning strategy for carbon options optimal trading, which can also be extended to stock options. …”
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Autonomous deployment and energy efficiency optimization strategy of UAV based on deep reinforcement learning by Yi ZHOU, Xiaoyong MA, Fuxiao GAO, Wei LI, Nan CHENG, Ning LU

“…Utilizing a UAV to build aerial mobile small cell can provide more flexible and efficient access services for ground terminal users.Constrained by the coverage and limited energy of the UAV,it is necessary to study how to build a fast,efficient and energy-saving air-ground collaborative network.To deal with complex dynamic scenarios,the UAV needs to deploy an optimal coverage position,and meanwhile reduce both path loss and energy consumption in the deployment process.Based on the deep reinforcement learning,a strategy of autonomous UAV deployment and efficiency optimization was proposed.The coverage state set of UAV was established,and the energy efficiency was used as a reward function.Depth neural network and Q-learning were used to guide UAV to make autonomous decision and deploy the optimal position.The simulation results show that the deployment time of the proposed method can be effectively reduced by 60%,while the energy consumption can be reduced by 10%~20%.…”
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Learning path planning methods based on learning path variability and ant colony optimization by Jing Zhao, Haitao Mao, Panpan Mao, Junyong Hao

“…The results show that the loss value of the ant colony optimization algorithm converges to a minimum value of 0.1, which has the best stability of the loss function curve and the fastest convergence speed compared to other algorithms. …”
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Optimizing the Hexagonal Fuzzy Transportation Problem With the Novel Dhouib-Matrix-TP1 Method by Souhail Dhouib, Aida Kharrat, Taicir Loukil, Habib Chabchoub

“…Transportation Problem (TP) is considered a combinatorial optimization problem, and its aim is to minimize the total transportation cost from several sources to different destinations. …”
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Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance Using Reinforcement Learning by Muhammad Nawaz Khan, Sokjoon Lee, Mohsin Shah

“…Cognitive sensors are always restricted in resources, and if careful strategy is not applied at the time of deployment, the sensors become disconnected, degrading the system’s performance in terms of energy, reconfiguration, delay, latency, and packet loss. To address these challenges and to establish a connected network, there is always a need for a system to evaluate the contents of detected data values and dynamically switch sensor states based on their function. …”
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Heuristic optimization in classification atoms in molecules using GCN via uniform simulated annealing by Agnieszka Polowczyk, Alicja Polowczyk, Marcin Woźniak

“…Experimental results confirm that our proposed optimization method outperformed other standalone SOTA optimization models, including gradient and heuristics methods, demonstrating in each case to lower loss function values, higher accuracy values for balanced dataset and higher AUC (macro) values for imbalanced dataset.…”
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Optimal Integration of Renewable Energy–Based Distributed Generation Units in Radial Distribution System by Le Chi Kien, Truong Van Hien, Hoang Do Ngoc Tram, Thai Dinh Pham

“…In this study, the main purpose is to address optimal nonlinear constrained problems with three targets in the multiobjective function (MOF) for minimizing (1) total power loss, (2) voltage deviation, and (3) the cost of purchasing energy from the main grid considering the uncertainties of solar irradiance and wind speed in the actual region in Binh Thuan Province, Vietnam. …”
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Flexible Reconfiguration for Optimal Operation of Distribution Network Under Renewable Generation and Load Uncertainty by Behzad Esmaeilnezhad, Hossein Amini, Reza Noroozian, Saeid Jalilzadeh

“…The uncertainty of the load and generation from renewable energies is planned to use their probability density functions via a scenario-based approach. The suggested optimization problem is solved using a metaheuristic approach based on the coati optimization algorithm (COA) due to the nonlinearity and non-convexity of the problem. …”
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The Novel Sequence Distance Measuring Algorithm Based on Optimal Transport and Cross-Attention Mechanism by Yanmin Yu, Yongcai Lai, Ping Yan, Haiying Liu

“…The corresponding hinge loss function of each triplet is minimized, and we develop an iterative algorithm to solve the optimal transport problem and the attention/ground distance metric parameters in an alternate way. …”
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Day-Ahead Optimal Dispatch for Active Distribution Network Considering Action Cost of Devices by Yuanyuan YUE, Zhuding WANG, Hui WANG, Xuan LUO, Zhou SU, Zijia HUI

“…Establish a daily optimal scheduling model with the objective function of minimizing the comprehensive operating cost and the constraints of Distflow branch power flow and equipment safety operation. …”
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Enhancing Image Quality by Optimizing and Fine-Tuning Multi-Fidelity Generative Adversarial Networks by Noor Baha Aldin

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Establishment and Test Effect of Artificial Intelligence Optimization Model Based on Convolutional Neural Network by Chunrong Zhou, Zhenghong Jiang

“…In addition, the authors optimized the convolutional layer, pooling layer, and loss function of AL-CNN in different parameters, which improved the stability of noise processing, respectively. …”
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Multi-objective Optimal Dispatch of Off-grid Integrated Hydrogen Energy Utilization System by Zhu Shihao, Hu Hongming, Du Banghua, Xie Changjun, Zhu Wenchao

“…By correlating the aging behaviors and lifetime to voltage degradation, a life-cycle operational cost function is derived for a multi-objective optimization (MOO) model. …”
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Optimization Technique for Renewable Energy Storage Systems for Power Quality Analysis with Connected Grid by R. Senthil Kumar, B. V. S. Acharyulu, P. K. Dhal, Richa Adlakha, Sonu Kumar, C. Saravanan, Krishna Bikram Shah

“…The goal of the multiobjective optimization dispatch (MOOD) problem is to lower overall operational costs as well as the costs associated with power loss in efficient conservation systems and exhaust emission quantities such as nitrogen oxides, sulphur dioxide, and carbon dioxide. …”
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Enhancing Drone Detection via Transformer Neural Network and Positive–Negative Momentum Optimizers by Pavel Lyakhov, Denis Butusov, Vadim Pismennyy, Ruslan Abdulkadirov, Nikolay Nagornov, Valerii Ostrovskii, Diana Kalita

“…The developed algorithms for training NN architectures improved the accuracy of drone detection by achieving the global extremum of the loss function in fewer epochs using positive–negative pulse-based optimization algorithms. …”
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