An Irregular Flight Scheduling Model and Algorithm under the Uncertainty Theory by Deyi Mou, Wanlin Zhao

“…The flight scheduling is a real-time optimization problem. Whenever the schedule is disrupted, it will not only cause inconvenience to passenger, but also bring about a large amount of operational losses to airlines. …”
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Dynamic bandwidth allocation algorithm based on transmission rate adaptation by Geng CHEN, Wei-wei XIA, Lian-feng SHEN

“…The adaptive bandwidth reallocation problem was formulated as an optimal problem and a dynamic optimal iterative procedure was used to adjust adaptively users' transmission rates to further maximize this utility function. …”
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A cost-effective adaptive repair strategy to mitigate DDoS-capable IoT botnets. by Jiamin Hu, Xiaofan Yang

“…By leveraging optimal control theory, we propose an iterative algorithm to solve the problem, numerically obtaining the learned time-varying parameters and a repair strategy. …”
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A Robust Control Method for the Trajectory Tracking of Hypersonic Unmanned Flight Vehicles Based on Model Predictive Control by Haixia Ding, Bowen Xu, Weiqi Yang, Yunfan Zhou, Xianyu Wu

“…Then, a robust model predictive controller is designed and the optimal control law is derived to address the trajectory tracking control problem under complex constraints such as parameter perturbations. …”
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Robust Beamforming for Superdirective Antenna Arrays and Experimental Validations by Mengying Gao, Haifan Yin, Liangcheng Han, Jingcheng Xie

“…To solve this optimization problem, we propose an efficient orthogonal complement-aided quadratic constraint least squares (OC-QCLS) algorithm, which achieves the maximum possible directivity under the sensitivity constraint. …”
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Game-Theoretic Cooperative Task Allocation for Multiple-Mobile-Robot Systems by Lixiang Liu, Peng Li

“…First, the task allocation problem for multiple mobile robots is formulated to optimize the resource utilization. …”
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UAV path intelligent planning in IoT data collection by Shu FU, Xiangyue YANG, Haijun ZHANG, Chen CHEN, Peng YU, Xin JIAN, Min LIU

“…To solve the problem of path planning of UAV data collection, it was generally be divided into global path planning and local path planning.For global path planning, it was modeled as an orientation problem, which was a combination of two classical optimization problems, the knapsack problem and the traveling salesman problem.The pointer network of deep learning was used to solve the model to obtain the service node set and service order under the energy constraint of the UAV.In terms of the local path planning, the reference signal strength (RSS) of the sensor node received by UAV was employed to learn the local flight path of UAV by deep Q network, which enabled the UAV to approach and serve the nodes.Simulation results show that the proposed scheme can effectively improve the revenue of UAV data collection under the energy constraint of UAV.…”
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Solving Steady-state Mono-energy Neutron Diffusion Solution Set with Parameterized Physics-informed Neural Network by XIE Yuchen, MA Yu, WANG Yahui

“…This work can be applied to the rapid solution of complex neutron diffusion problems. Future work will focus on further optimizing this method based on parameterized PINNs to improve its performance in handling more complex problems. …”

Research on Automatic Recovery Strategy for Metro Train Delay by QING Guangming, LI Jianghong, ZHANG Zhaoyang, ZHANG Yu

“…Combined with the constraints such as train safety operation and service demand, a mathematical model and a solution method for the adjustment and optimization of the delayed operation of the train were established. …”
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Multi-Ground Station Collaborative Measurement and Control Technology for Giant Constellation System by Yang LIU, Di ZHOU, Min SHENG, Jiandong LI, Shiguang HAO, Xiaotian ZHENG

“…Measurement and control technology is the key technology to ensure the effi cient operation, maintenance and management of the constellation system.In recent years, with the continuous expansion of the constellation scale, mega-constellation system has gradually formed, which makes the demand for constellation measurement and control show an explosive growth, which puts forward new requirements for the completion of the constellation system measurement and control tasks.Firstly, the constraints of the megaconstellation system measurement and control tasks and the equipment constraints of the ground measurement and control station were analyzed, and the problem modeling was given; Secondly, an interaction method of the ground station agent based on the learning planning segment was proposed, by introduced the constraint penalty operator and the multi-ground station joint the penalty operator was designed to optimized the objective function.Finally, a multi-ground station Agent reinforcement learning algorithm was proposed to solved the multi-ground station cooperative task assignment strategy.Simulation experiments showed that when the task scale was large, the method had a gain of 12%~20% compared with the traditional algorithm in the diff erent scenarios mentioned.…”
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Multi-Ground Station Collaborative Measurement and Control Technology for Giant Constellation System by Yang LIU, Di ZHOU, Min SHENG, Jiandong LI, Shiguang HAO, Xiaotian ZHENG

“…Measurement and control technology is the key technology to ensure the effi cient operation, maintenance and management of the constellation system.In recent years, with the continuous expansion of the constellation scale, mega-constellation system has gradually formed, which makes the demand for constellation measurement and control show an explosive growth, which puts forward new requirements for the completion of the constellation system measurement and control tasks.Firstly, the constraints of the megaconstellation system measurement and control tasks and the equipment constraints of the ground measurement and control station were analyzed, and the problem modeling was given; Secondly, an interaction method of the ground station agent based on the learning planning segment was proposed, by introduced the constraint penalty operator and the multi-ground station joint the penalty operator was designed to optimized the objective function.Finally, a multi-ground station Agent reinforcement learning algorithm was proposed to solved the multi-ground station cooperative task assignment strategy.Simulation experiments showed that when the task scale was large, the method had a gain of 12%~20% compared with the traditional algorithm in the diff erent scenarios mentioned.…”
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DC algorithm for estimation of sparse Gaussian graphical models. by Tomokaze Shiratori, Yuichi Takano

“…To this end, we focus on sparse estimation of GGM with the cardinality constraint based on the ℓ0 norm. Specifically, we convert the cardinality constraint into an equivalent constraint based on the largest-K norm, and reformulate the resultant constrained optimization problem into an unconstrained penalty form with a DC (difference of convex functions) representation. …”
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Systems resource allocation algorithm for RIS-assisted D2D secure communication based on parallel CNN by Zhengyu ZHU, Gengwang HOU, Chongwen HUANG, Gangcan SUN, Wanming HAO, Jing LIANG

“…To meet the requirements of intelligent signal processing and physical layer security, aiming at the shortage of spectrum resources, a resource allocation algorithm for reconfigurable intelligent surface (RIS)-assisted the device to device (D2D) communication was proposed.D2D users communicated by multiplexing the spectrum resources of cellular users.Considering the constraints of D2D transmission rate, base station transmission power and RIS transmission phase shift, the problem of maximizing user security rate was formulated.To solve the nonlinear programming problem, a parallel convolutional neural network (CNN) algorithm was proposed to obtain the optimal resource allocation scheme.Simulation results show that the parallel CNN algorithm can effectively improve the secrecy rate and it is significantly better than other benchmark algorithms.…”
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Application of Combinatorial Interaction Design for DC Servomotor PID Controller Tuning by Mouayad A. Sahib, Bestoun S. Ahmed, Moayad Y. Potrus

“…It was also found to be a successful approach that can be applied to solve other similar problems in different fields of research. In line with this approach, this paper presents a new application of the combinational optimization in the design of PID controller for DC servomotor. …”
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APPROACH TO OPTIMISING AND MANAGING WAREHOUSE STOCKS IN A CAR SERVICE by Ivan Beloev, Dimitar Grozev

“…The objective of the proposed model is to minimise the total inventory costs, including purchasing, holding, and potential shortage costs, by applying integer linear programming techniques. The problem is formulated with decision variables representing quantities to be ordered and stocked, and includes a set of linear constraints reflecting all operational requirements. …”
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Resource allocation in cognitive radio network with energy harvesting by Yan LONG, Xiaoqian ZHANG, Xuming FANG, Rong HE

“…Considering the diversity of energy harvesting capability and spectrum sensing accuracy of SU,as well as dynamic channel quality,under the constraint of energy causality,the secondary network throughput maximization problem in single-hop cognitive radio networks with energy harvesting was studied.The transmission channel selection,transmission power control and transmission time allocation of SU were jointly optimized.Since the optimization problem was non-convex,by converting it into a series of convex optimization sub-problems,the optimize transmission power and transmission time algorithm (OPTA) was obtained.Compared with the existing resource allocation algorithms,such as,hybrid differential evolution algorithm (HDEA),optimized transmission algorithm (OTA),and random assignment channel algorithm (RA),the simulation results verify the correctness and effectiveness of the proposed algorithm.For example,under the same maximum transmission power constraint,the throughput of the proposed OPTA scheme could increase by around 6%,37% and 50% than that of HDEA,OTA and RA schemes respectively.Under the same channel gain diversity,the throughput of the proposed OPTA scheme could increase by around 30%,60% and 94% than that of HDEA,OTA and RA schemes respectively.Under the same energy harvesting efficiency diversity,the throughput of the proposed OPTA scheme could increase by around 27%,50% and 92% than that of HDEA,OTA and RA schemes respectively.…”
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Waste Collection Optimisation: A Path to a Green and Sustainable City of Makkah by Haneen Algethami, Ghada Talat Alhothali

“…Thus, there is a need to find optimal and fast solutions to this problem. Solving this problem demands tackling numerous routing constraints while aiming to minimise the operational cost. …”
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Energy-efficient power control for two-tier femtocell networks with block-fading channels by Zhixin Liu, Jieshuai Wu, Yang Liu, Hongjiu Yang, Jianfeng Guan, Yu Wang

“…In order to enhance the robustness of the two-tier network, we consider the imperfect channel state information of interference links in block-fading channels using probability constraints. We propose a novel scheme to derive optimal energy efficiency by transforming probability constraints into indicator function. …”
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A New Method for Solving Multiobjective Bilevel Programs by Ying Ji, Shaojian Qu, Zhensheng Yu

“…This new model gives rise to a new Pareto optimum concept, which we call “robust-weighted Pareto optimum”; for the worst-case weighted multiobjective optimization with the weight set of each player given as a polytope, we show that a robust-weighted Pareto optimum can be obtained by solving mathematical programing with equilibrium constraints (MPEC). …”
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Intra-Net Cognitive Radio Intelligent Utility Maximization using Adaptive PSO-Gradient Algorithm by Imran Ullah Khan

“… Artificial intelligence now days are mainly dependent on deep learning techniques as it is rapidly growing and capable to outperform other approaches and even human at various problems. Intelligently utilizing resources that meets the growing need of demanding services as well as user behavior is the future of wireless communication systems. …”
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