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IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Chen, Guoquan Wu, Shaohua Deng, Yajing Jiao, Jian Zhang, Qinyu Harbin Inst Technol Shenzhen Dept Elect & Infor mation Engn Shenzhen 518055 Peoples R China Harbin Inst Technol Shenzhen Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

ISBN: (纸本)9798350310900

Recently, the Space-Air-Ground-Sea Integrated Network (SAGSIN) attracts great attention due to its ability to provide high-speed communication services to aviation users (AUs) and marine users (MUs), with Low Earth Orbit (LEO) satellites play an essential role. However, the available space in LEO is nearly saturated and full of massive space junks, which, combined with the ultra-low latency requirements for future 6G, presents a significant challenge. To address this issue, we propose designing a Very Low Earth Orbit (VLEO)-based satellite network that efficiently serves AUs and MUs. We first create and analyze the heat maps based on actual collected data of Chinese aviation and marine communication traffic, and generate a benchmark observation point model with grid point method. Then we propose an implicit multi-objective continuous multi-variate optimization problem to achieve the maximum average coverage with minimum VLEO satellites. To solve this problem, we build a satellite constellation simulation system, using the idea of decomposition and polymerization combined with the elite strategic genetic algorithm (ESGA) of swarm intelligence optimization algorithm. Many simulation results are obtained, including the indication that the optimal VLEO constellation has the deployment features of large altitude and low inclination, and has better coverage performance for longitudinal distributed business. The design process in this work is highly migratory.

关键词： SAGSIN VLEO satellite constellation swarm intelligence optimization algorithm ESGA

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Landslide Susceptibility Mapping based on swarm optimization Gradient Boosting Decision Tree in Sanshui District(China) 4

Landslide Susceptibility Mapping based on Swarm Optimization...

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4th IEEE International Conference on Software Engineering and Artificial intelligence (SEAI)

作者： Liu, Zhuang Duan, Gonghao Wuhan Inst Technol China Hubei Prov Key Lab Intelligent Robot Sch Comp Sci & Engn Artificial Intelligence Wuhan Peoples R China

ISBN: (纸本)9798350374353;9798350374346

This paper takes Sanshui District of Foshan City as the research object and proposes two GBDT(Gradient Boosted Decision Tree) models based on swarm optimization algorithm. Firstly, seven landslide influencing factors were selected as evaluation indexes. Secondly, the cost matrix was introduced at the algorithm level according to the imbalance of positive and negative samples of landslides. Finally, GBDT, ACO-GBDT(Ant Colony optimization for Gradient Boosted Decision Tree) and GWO-GBDT(Grey Wolf Optimizer for Gradient Boosted Decision Tree) were used to carry out landslide susceptibility mapping. The results show that:(1) The spatial distribution of landslides is controlled by multilevel factors. (2) The model prediction accuracy is: GWO-GBDT(AUC = 0.92) > ACO-GBDT(AUC = 0.90) > GBDT(AUC = 0.85). Therefore, the swarm optimization algorithm is effective for the optimization of GBDT model and improves the prediction performance of the model.

关键词： GBDT swarm intelligence optimization algorithm Landslide Susceptibility Mapping Imbalance of Landslide Samples

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A Deep Reinforcement Learning-Based algorithm for Multi-Objective Agricultural Site Selection and Logistics optimization Problem

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APPLIED SCIENCES-BASEL 2024年第18期14卷 8479页

作者： Liu, Huan Zhang, Jizhe Zhou, Zhao Dai, Yongqiang Qin, Lijing Gansu Agr Univ Coll Informat Sci & Technol Lanzhou 730070 Peoples R China

The challenge of optimizing the distribution path for location logistics in the cold chain warehousing of fresh agricultural products presents a significant research avenue in managing the logistics of agricultural products. The goal of this issue is to identify the optimal location and distribution path for warehouse centers to optimize various objectives. When deciding on the optimal location for a warehousing center, various elements like market needs, supply chain infrastructure, transport expenses, and delivery period are typically taken into account. Regarding the routes for delivery, efficient routes aim to address issues like shortening the overall driving distance, shortened travel time, and preventing traffic jams. Targeting the complex issue of optimizing the distribution path for fresh agricultural products in cold chain warehousing locations, a blend of this optimization challenge was formulated, considering factors like the maximum travel distance for new energy trucks, the load capacity of the vehicle, and the timeframe. The Location-Route Problem with Time Windows (LRPTWs) Mathematical Model thoroughly fine-tunes three key goals. These include minimizing the overall cost of distribution, reducing carbon emissions, and mitigating the depletion of fresh agricultural goods. This study introduces a complex swarm intelligence optimization algorithm (MODRL-SIA), rooted in deep reinforcement learning, as a solution to this issue. Acting as the decision-maker, the agent processes environmental conditions and chooses the optimal course of action in the pool to alter the environment and achieve environmental benefits. The MODRL-SIA algorithm merges a trained agent with a swarm intelligence algorithm, substituting the initial algorithm for decision-making processes, thereby enhancing its optimization efficiency and precision. Create a test scenario that mirrors the real situation and perform tests using the comparative algorithm. The experimental findings indic

关键词： deep reinforcement learning Vehicle Routing Problem location problem swarm intelligence optimization algorithm fresh agricultural product supply chain

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Dynamic Gaussian mutation beetle swarm optimization method for large-scale weapon target assignment problems

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APPLIED SOFT COMPUTING 2024年 162卷

作者： Xu, Han Zhang, An Bi, Wenhao Xu, Shuangfei Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China

The weapon target assignment is a crucial issue for firepower resources optimization in modern warfare. Such a problem is complicated, multi-constrained, strongly nonlinear, NP-complete and the existing studies did not consider the suitability between different weapons and targets. In this paper, a novel weapon target assignment model is established that involves the weapon-target suitability and is closer to the real combat scenarios. Then, in view of that the conventional weapon target assignment methods are difficult to be applied in the large-scale problems efficiently, this work proposes a dynamic Gaussian mutation beetle swarm optimization algorithm with rule-based chaotic initialization. With the assistance of the dynamic parameter adjustment strategies and Gaussian mutation, the improved algorithm has fast convergence speed and high convergence accuracy, and it can solve the weapon target assignment problems with excellent optimization capabilities. Besides, the rulebased chaotic initialization strategy is embedded in this algorithm to generate high-quality population with better diversity. Finally, two comparative simulation cases of different initialization methods and algorithms for solving the large-scale weapon target assignment problems are designed. The results demonstrate that the proposed approach can provide more superior assignment schemes than its competitors with enhanced efficiency.

关键词： Weapon target assignment swarm intelligence optimization algorithm Beetle swarm optimization Nonlinear integer programming

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Research on many-to-many target assignment for unmanned aerial vehicle swarm in three-dimensional scenarios

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COMPUTERS & ELECTRICAL ENGINEERING 2021年 91卷 107067-107067页

作者： Hua, Xiang Wang, Zhao Yao, Hongjuan Li, Baohua Shi, Chenglong Zuo, Jiaxian Xian Technol Univ Sch Def Sci & Technol Xian 710021 Shaanxi Peoples R China Xian Technol Univ Sch Elect Informat Engn Xian 710021 Shaanxi Peoples R China

Target assignment for unmanned aerial vehicle (UAV) swarm has been a research hotspot in academic and industry communities. The current methods mainly focus on multiple targets assignment in planes or few targets assignment in solids. However, they ignore three-dimensional scenarios for UAV swarm and multiple targets characteristics for assignment problem. To solve these issues, we propose a multi-target intelligent assignment model. Firstly, we introduce damage cost and time cost to evaluate the system performance of swarm and time performance in three-dimensional scenarios. Then, we design a bio-inspired swarm intelligence optimization algorithm to find the optimal multiple targets assignment and to balance the two costs and multiple constraints simultaneously. This algorithm regards UAVs as several parallel biological sub-populations, which adopts the multi-layered optimization strategy to select the suitable assignment sequence. The simulation results demonstrate that the proposed method is effective for multi-target assignment in three-dimensional scenarios.

关键词： Unmanned aerial vehicle Target assignment swarm intelligence optimization algorithm Three-dimensional scenario

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A comprehensive survey of the application of swarm intelligent optimization algorithm in photovoltaic energy storage systems

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SCIENTIFIC REPORTS 2024年第1期14卷 1-30页

作者： Wang, Shuxin Yue, Yinggao Cai, Shaotang Li, Xiaojuan Chen, Changzu Zhao, Hongliang Li, Tiejun Shanghai Zhongqiao Vocat & Tech Univ Sch Intelligent Mfg Shanghai 201514 Peoples R China Wenzhou Univ Technol Sch Intelligent Mfg & Elect Engn Wenzhou 325035 Peoples R China State Grid Chongqing Elect Power Co Informat & Commun Branch Chongqing 400014 Peoples R China Zhejiang Carspa New Energy LTD Wenzhou 325035 Peoples R China

With the rapid development of renewable energy, photovoltaic energy storage systems (PV-ESS) play an important role in improving energy efficiency, ensuring grid stability and promoting energy transition. As an important part of the micro-grid system, the energy storage system can realize the stable operation of the micro-grid system through the design optimization and scheduling optimization of the photovoltaic energy storage system. The structure and characteristics of photovoltaic energy storage system are summarized. From the perspective of photovoltaic energy storage system, the optimization objectives and constraints are discussed, and the current main optimization algorithms for energy storage systems are compared and evaluated. The challenges and future development of energy storage systems are briefly described, and the research results of energy storage system optimization methods are summarized. This paper summarizes the application of swarm intelligence optimization algorithm in photovoltaic energy storage systems, including algorithm principles, optimization goals, practical application cases, challenges and future development directions, providing new ideas for better promotion and application of new energy photovoltaic energy storage systems and valuable reference.

关键词： Photovoltaic energy storage system Battery energy storage Optimal scheduling swarm intelligence optimization algorithm Application

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Study on Discrimination Model and Application of Mine Water Inrush Source Based on swarm Intelligent Neural Network

Study on Discrimination Model and Application of Mine Water ...

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第九届世界华人地质科学研讨会

作者： XU Xing SHI Pingzhou WANG Xingzhi LI Yuanzhi ZHANG Ruilin School of Safety Engineering Henan University of Engineering School of geoscience and technology Southwest Petroleum University

Mine water inrush is one of the most threatening natural disasters in the process of mine construction and production. Once mine water inrush occurs, how to quickly determine the cause of water inrush and find out the source of water inrush is the key to solve and further prevent the water inrush disaster. Because hydrochemical data is the most essential feature of groundwater, it is fast, accurate and economical to discriminate the source of mine water inrush from water quality data. Starting from the frontier disciplines of artificial intelligence, bionics, chaos theory, mathematical statistics and computer programming language, BP neural network with nonlinear mapping function was introduced into the discrimination of groundwater hydration characteristic components. At the same time, particle swarm optimization（PSO） intelligent optimization algorithm was used to globally optimize the initial weight and threshold of neural network, it could improve the convergence speed, avoid over-fitting and improve the generalization of neural network in the training process of neural network. In order to overcome the "premature" convergence of PSO, an improved particle swarm optimization algorithm（MPSO） was proposed by improving the parameters of inertia weight, cognitive coefficient and social coefficient, random mutation operator and so on. Adaptive chaotic particle swarm optimization（ACPSO） was proposed by introducing the "premature" judgment mechanism and chaotic mapping principle into PSO. MATLAB software was used to design and compile the program, four kinds of nonlinear water inrush source discrimination models, BP, PSO-BP, MPSO-BP and ACSO-BP, were constructed. The application results show that the neural network based on swarm intelligence optimization improves the discrimination accuracy of mine water inrush source, but compared with each other, ACPSO-BP is better than MPSO-B, MPSO-BP is better than PSO-BP, PSO-B is better than BP in convergence speed, accuracy and g

关键词： mine water inrush source hydration feature extraction BP neural network swarm intelligence optimization algorithm nonlinear discrimination

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optimization algorithm of Spotted Hyena Based on Chaotic Reverse Learning Strategy 22

Optimization Algorithm of Spotted Hyena Based on Chaotic Rev...

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Proceedings of the 2022 5th International Conference on Artificial intelligence and Pattern Recognition

作者： Xu He Hengzhi Lu Zixing Ling School of Artificial Intelligence Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

The application of swarm optimization algorithm in WSNs has become a new research hotspot of scholars at home and abroad. Aiming at the problem that the spotted hyena optimization algorithm is easy to fall into local optimum, which leads to low optimization accuracy, an improved spotted hyena optimization algorithm is proposed. On the basis of the original algorithm, Sine chaotic map and elite reverse learning strategy are embedded to reduce the probability of falling into local optimum and improve the global search ability of spotted hyena optimization algorithm. In addition, the adaptive inertia weight is introduced to balance the global search and local development capabilities of the spotted hyena optimization algorithm. The experimental results show that compared with the original spotted hyena optimization algorithm, sine and cosine algorithm, multiverse optimization algorithm, differential evolution algorithm and particle swarm optimization algorithm, the improved algorithm has significant performance advantages in optimization ability and stability.

关键词： Elite reverse learning strategy Inertia weight Sine chaotic mapping Spotted hyena optimization algorithm swarm intelligence optimization algorithm

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Flamingo Search algorithm and Its Application to Path Planning Problem 21

Flamingo Search Algorithm and Its Application to Path Planni...

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Proceedings of the 2021 4th International Conference on Artificial intelligence and Pattern Recognition

作者： Zhiheng Wang Jianhua Liu School of Computing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

After studying the migratory and foraging behavior of flamingos, this paper proposes a new swarm intelligence optimization algorithm: flamingos search algorithm. At the same time, in order to verify the optimization effect and stability of FSA, this paper conducts tests and comparisons on 20 different benchmark functions, and standard deviation of the proposed FSA outperformed the other algorithms with regard to 20 test functions and path planning applications. Finally, this paper applies FSA to the path optimization problem, and the test results show that FSA has good engineering application potential, especially in the solution of path planning problems with good optimization performance. Experimental code for this article can be obtained from this website: https://***/18280426650/FSA.

关键词： Constrained optimization Flamingo search algorithm Path planning problem swarm intelligence optimization algorithm

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