检索结果-内蒙古大学图书馆

An obstacle aware efficient MANET routing with optimized Bi-LSTM and multi-objective constraints on improved heuristic algorithm

引用

JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 2023年第4期15卷 357-380页

作者： Bhavadharini, R. M. Beaulah, P. Mercy Rajaselvi Kumar, C. U. Om Krithiga, R. Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India

Mobile Ad Hoc Networks (MANETs) are self-organizing, self-configuring, and infrastructure-less networks for performing multi-hop communication. The source mobile node can transmit the information to any other destination node, but it has limitations with energy consumption and battery lifetime. Since it appeals to a huge environment, there is a probability of obstacle present. Thus, the network requires finding the obstacles to evade performance degradation and also enhance the routing efficiency. To achieve this, an obstacle-aware efficient routing using a heuristic-based deep learning model is proposed in this paper. Firstly, the nodes in the MANET are employed for initiating the transmission. Further, it is needed to be predicted whether the node is malicious or not. Consequently, the prediction for link connection between the nodes is achieved by the Optimized Bi-directional Long-Short Term Memory (OBi-LSTM), where the hyperparameters are tuned by the Adaptive Horse Herd Optimization (AHHO) algorithm. Secondly, once the links are secured from the obstacle, it is undergone for routing purpose. Routing is generally used to transmit data or packets from one place to another. To attain better routing, various objective constraints like delay, distance, path availability, transmission power, and several interferences are used for deriving a multi-objective function, in which the optimal path is obtained through the AHHO algorithm. Finally, the simulation results of the proposed model ensure to yield efficient multipath routing by accurately identifying the intruder present in the network. Thus, the proposed model aims to reduce the objectives like delay, distance, and power consumption.

关键词： Mobile ad hoc networks obstacle-aware routing link connectivity prediction adaptive horse herd optimization algorithm optimized bi-directional long-short term memory optimal routing multi-objective constraints

来源：评论

学校读者我要写书评

暂无评论

An FTSA Trajectory Elliptical Homotopy for Unmanned Vehicles Path Planning With multi-objective constraints

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

引用

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第3期8卷 2415-2425页

作者： Fu, Jinyu Yao, Weiran Sun, Guanghui Tian, Haoyu Wu, Ligang Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Peoples R China

This article investigates a fixed-time simultaneous arrival (FTSA) problem in terms of the equilibrium of path lengths of unmanned vehicles. A novel trajectory elliptical homotopy method (TEHM) is designed to solve the FTSA problem of unmanned vehicles in a multi-objective constrained environment. Considering the constraints of obstacle avoidance and kinematics of unmanned vehicles, the trajectories elliptical homotopy is selected for path planning. The obtained trajectory homotopy guarantees obstacle avoidance and motion stability at the same time. To handle the non-cooperative and dynamic obstacle avoidance, a trajectory elliptical homotopy decomposition (TEHD) algorithm is proposed with an FTSA constraint. Based on the TEHM and TEHD, a multiple unmanned vehicle fixed-time regular-triangle formation algorithm is designed and implemented on real vehicles. Simulations and experiments validate the performance of the proposed methods and show how fixed-time arrival formation under obstacles and kinematic constraints was obtained.

关键词： Trajectory Autonomous vehicles Planning Kinematics Turning Heuristic algorithms Collision avoidance Fixed-time simultaneous arrival elliptical homotopy trajectory decomposition multi-objective constraints

来源：评论

学校读者我要写书评

暂无评论

A Hybrid A* Path Planning Algorithm Based on multi-objective constraints

A Hybrid A* Path Planning Algorithm Based on Multi-objective...

引用

Asia Conference on Advanced Robotics, Automation, and Control Engineering (ARACE)

作者： Zhao, Yu Zhu, Yongqiang Zhang, Pingxia Gao, Qi Han, Xue Qingdao Univ Technol Sch Mech & Automot Engn Qingdao Peoples R China

ISBN: (数字)9781665451536

ISBN: (纸本)9781665451536

To provide a safe, smooth and efficient global planning path for nonholonomic mobile robots. Aiming at the problems of traditional hybrid A* algorithm in path planning, such as approaching obstacles, unnecessary reversing and redundant turning, a multi-objective constraint method based on hybrid A* algorithm is proposed To speed up the path planning, the heuristic function is dynamically weighted and the overall path cost function is designed The path planning experiments are carried out in ROS (Robot Operation System) simulation environment and actual environment respectively, and the results show that. The hybrid A* algorithm with multi-objective constraints increases the minimum distance between the robot and obstacles by more than 50%, reduces the unnecessary times of reversing and turning, and reduces the total running time by 14.2% on average, thus improving the navigation efficiency of the mobile robot.

关键词： nonholonomic constrained robot multi-objective constraints hybird A path planning

来源：评论

学校读者我要写书评

暂无评论

Exploring the Optimal Allocation Decision-Making of Expenditure Budget in Hospitals Under multi-objective constraints: Evidence from Urban Public Hospitals, China

引用

SAGE OPEN 2021年第4期11卷

作者： Ren, Weicun Zhao, Yizhen Zhong, Huiliang Fu, Xiaoli Wu, Jian Xinxiang Med Univ Sanquan Coll Urumqi Peoples R China Zhengzhou Univ Zhengzhou Henan Peoples R China Luoyang Zhenggu Orthoped Hosp Henan Prov Luoyang Peoples R China Henan Nursing Vocat Coll Anyang Peoples R China

Hospitals in many countries face the need for balancing different categories of expenditures to achieve multiple goals within a limited budget. This study established a two-stage fuzzy linear programming (FLP) estimation model to explore the optimal allocation decision-making of expenditure budget under the multi-objective constraints. Taking all urban public hospitals in Henan province of China as a sample, the optimal allocation decision-making of total expenditure budget was tested with the human resources expenditures (HE) as the dependent variable. And the outcome was compared with the actual expenditure data of these hospitals between 2010 and 2016. The study found that when the HE achieves the maximum and minimum feasible scale, the expenditure scales of the budget allocation categories including pharmaceutical expenditures, medical supplies expenditures, and other expenditures were all within a reasonable range. Among them, the observed promoting space for HE was 3.78 billion yuan. The results show that the FLP method can help urban public hospitals to make better total expenditure budget allocation decisions, which can maintain their reasonable expenditure structure under the hospitals' development goals and the government's regulatory requirements.

关键词： FLP hospital expenditure decision-making multi-objective constraints

来源：评论

学校读者我要写书评

暂无评论

Simulation of high-dimensional collaborative distribution of disaster materials with time window constraints

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期44卷 7543-7552页

作者： Xu, Baohua Chen, Jiayu Li, Zhi Yang, Tao State Grid Anhui Procurement Co Hefei 361024 Anhui Peoples R China Anhui Jiyuan Software Co Ltd Hefei Anhui Peoples R China

In recent years, with the continuous occurrence of natural disasters, people have gradually realized the importance of improving emergency response capability, and the weight of time constraints for rational allocation of emergency materials has gradually increased. Therefore, a high-dimensional collaborative allocation method of disaster materials with time window constraints is studied. A high-dimensional collaborative distribution model of disaster materials with time window constraints is constructed by combining four dimensional decision-making indexes: maximizing the satisfaction of material demand, fairness of material distribution and minimizing the total cost of expected emergency response;Build SPEA2 + SDE hybrid algorithm, solve the model and output the optimal solution set. The simulation results show that this method can have the ability of high-dimensional distribution of disaster materials, obtain the output of the optimal distribution scheme set of disaster materials, and the material satisfaction is more than 0.70. Under the condition of minimum distribution cost, the distribution of disaster materials can be completed.

关键词： Time windowconstraint disaster materials high dimensional collaborative allocation multi-objective constraints decision index

来源：评论

学校读者我要写书评

暂无评论

Swarm Intelligence-Inspired Meta-Heuristics Hybrid Optimization for multi-Constraint Routing in Vehicular Adhoc Networks

引用

IETE JOURNAL OF RESEARCH 2024年第1期70卷 95-115页

作者： Suganyadevi, K. Nandalal, V. Sri Eshwar Coll Engn Elect & Commun Engn Dept Coimbatore Tamilnadu India Sri Krishna Coll Engn & Technol Dept Elect & Commun Engn Coimbatore Tamilnadu India

In recent years, the popularity of vehicle adhoc networks (VANET) in wireless intelligent transportation systems has significantly increased. It is difficult to determine the quickest path between the source and the target in a VANET traffic system. Longer routes feature increased network overhead, more expensive connections, more path failures, and worse routing efficiency. Identifying the shortest route in optimization, often known as the Travelling Salesman Problem (TSP) and traffic congestion, is a well-known combinatorial optimization challenge with various practical applications. To improve routing efficiency, the HACOSMO (Hybrid-Ant Colony Optimization with Spider Monkey Optimization) system's recommended meta-heuristic approach finds the shortest path using distance and traffic based principles. The simulation (NS-2) outcomes focused on the efficiency of the proposed method beats the other existing methods like ACO, GRACO, OACO, and IDBACOR in terms of overhead, throughput, latency, packet failure, and message transmission ratio. According to HACOSMO, routing overhead is 8% to 11% under IDBACOR, 10% to 14% under GRACO,19% to 31% under OACO, and 24% to 37% under ACO for a variety of vehicle counts and speed ranges. When compared to IDBACOR, the proposed method enhances throughput by 3% to 7%, 6% to 8% when compared to GRACO, 11% to 15% when compared to OACO, and 16% to 25% when compared to ACO.

关键词： VANET Shortest path multi-objective constraints ACO optimization Traffic prediction Data transmission

来源：评论

学校读者我要写书评

暂无评论

Intelligent planning of fire evacuation routes in buildings based on improved adaptive ant colony algorithm

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2024年 194卷

作者： Zhang, Yan-Qi Wang, Jun-Hui Wang, Yi Jia, Zhi-Chao Sun, Qian Pei, Qiu-Yan Wu, Dong Taiyuan Univ Technol Coll Safety & Emergency Management Engn Taiyuan 030024 Peoples R China China Inst Radiat Protect Nucl Emergency & Nucl Safety Dept Taiyuan 030006 Peoples R China China Univ Min & Technol Sch Mines Xuzhou 221116 Peoples R China

Intelligent planning of fire evacuation routes is an important guarantee for rapid emergency response. Ant colony optimization, as an intelligent bionic algorithm, has notable advantages in route planning. However, traditional ant colony optimization corresponds to a low convergence rate, is easily caught in local optimal solution, and regards route length as the only constraint. To resolve these problems, an improved adaptive ant colony optimization (IAACO) algorithm was proposed in this study. Risk, energy consumption, and route length were taken as key factors to improve the heuristic function, optimize the pheromone update function, and establish multiobjective constraints, The standards for fire evacuation better align with practical requirements. Meanwhile, the adaptive pheromone volatile coefficient was introduced to balance convergence and global searching ability. In addition, the hazard range on the grid map was visualized. The results indicate that under various complex obstacle grid maps, the path inferiority of IAACO is reduced by 61.7% and 58.4%, 43.6% and 36.7%, and 41.6% and 67.7% compared to ACO and IACO, respectively;under the condition of multiple exits, the inferiority is reduced by 63.8% and 54.6%;under the condition of multiple fire sources, the inferiority is reduced by 40.1% and 34.6%;compared with other algorithms, IAACO shows the lowest path inferiority index, 26.6. IAACO is applicable to both the dynamic planning of fire evacuation routes and the evacuation simulation software, Pathfinder, and it performs better than the built-in algorithms of Pathfinder. Facts have proved that the IAACO algorithm significantly improves the safety level of evacuation compared to traditional evacuation methods and other optimization algorithms.

关键词： Building fires Path planning Ant colony optimization multi-objective constraints Volatile coefficient

来源：评论

学校读者我要写书评

暂无评论

An efficient multi-objective scheduling algorithm based on spider monkey and ant colony optimization in cloud computing

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第2期27卷 1799-1819页

作者： Amer, Dina A. Attiya, Gamal Ziedan, Ibrahim Higher Technol Inst Comp Sci Dept Tenth Of Ramadan City Egypt Menoufia Univ Fac Elect Engn Comp Sci & Engn Dept Menoufia Egypt Zagazig Univ Fac Engn Comp & Syst Engn Dept Zagazig Egypt

Due to easier access, improved performance, and lower costs, the use of cloud services has increased dramatically. However, cloud service providers are still looking for ways to complete users' jobs at a high speed to increase profits and reduce energy consumption costs. To achieve such a goal, many algorithms for scheduling problem have been introduced. However, most techniques consider an objective in the scheduling process. This paper presents a new hybrid multi-objective algorithm, called SMO_ACO, for addressing the scheduling problem. The proposed SMO_ACO algorithm combines Spider Monkey Optimization (SMO) and Ant Colony Optimization (ACO) algorithm. Additionally, a fitness function is formulated to tackle 4 objectives of the scheduling problem. The proposed fitness function considers parameters like schedule length, execution cost, consumed energy, and resource utilization. The proposed algorithm is implemented using the Cloud Sim toolkit and evaluated for different workloads. The performance of the proposed technique is verified using several performance metrics and the results are compared with the most recent existing algorithms. The results prove that the proposed SMO_ACO approach allocates resources efficiently while maintaining cloud performance that increases profits.

关键词： Cloud computing multi-objective constraints Task Scheduling SMO algorithm

来源：评论

学校读者我要写书评

暂无评论

An effective attack detection framework using multi-scale depth-wise separable 1DCNN via fused grasshopper-based lemur optimizer in IoT routing system

引用

INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第3期18卷 1741-1762页

作者： Marapatla, Ajay Dilip Kumar Ilavarasan, E. Pondicherry Technol Univ Dept Comp Sci & Engn Pondicherry 605014 India

A secured IoT routing model against different attacks has been implemented to detect attacks like replay attacks, version attacks, and rank attacks. These attacks cause certain issues like energy depletion, minimized packet delivery, and loop creation. By mitigating these issues, an advanced attack detection approach for secured IoT routing techniques with a deep structured scheme is promoted to attain an efficient attack detection rate over the routing network. In the starting stage, the aggregation of data is done with the help of IoT networks. Then, the selected weighted features are subjected to the multiscale Depthwise Separable 1-Dimensional Convolutional Neural Networks (MDS-1DCNN) approach for attack detection, in which the parameters in the 1-DCNN are tuned with the aid of Fused Grasshopper-aided Lemur Optimization Algorithm (FG-LOA). The parameter optimization of the FG-LOA algorithm is used to enlarge the efficacy of the approach. Especially, the MDS-1DCNN model is used to detect different attacks in the detection phase. The attack nodes are mitigated during the routing process using the developed FG-LOA by formulating the fitness function based on certain variables such as shortest distance, energy, path loss and delay, and so on in the routing process. Finally, the performances are examined through the comparison with different traditional methods. From the validation of outcomes, the accuracy value of the developed attack detection model is 96.87%, which seems to be better than other comparative techniques. Also, the delay analysis of the routing model based on FG-LOA is 17.3%, 12.24%, 10.41%, and 15.68% more efficient than the classical techniques like DHOA, HBA, GOA, and LOA, respectively. Hence, the effectualness of the offered approach is more enriched than the baseline approaches and also it has mitigated diverse attacks using secured IoT routing and different attack models.

关键词： Attack detection secured Iot routing fused grasshopper-based lemur optimization algorithm multiscale Depthwise Separable 1-Dimensional Convolutional Neural Networks multi-objective constraints multi-objective constraints

来源：评论

学校读者我要写书评

暂无评论

Unmanned aerial vehicle for waste management system using genetic algorithms

引用

Arab Journal of Basic and Applied Sciences 2025年第1期32卷 57-80页

作者： Hejres, Shaima Mahjoub, Amine Hewahi, Nabil College of Information Technology University of Bahrain Zallaq Bahrain

Waste management system is an important economic sector aiding in supporting health and improving the quality of life in smart cities. This paper proposes a multiobjective genetic algorithm (GA) aiming at improving the waste collection process through identifying the best appropriate tours that can minimize the cost. The proposed system aims to satisfy multi-objective approach addressing constraints with a single Unmanned Aerial Vehicle (UAV). It aims at improving performance by reducing flight distance and power consumption while maximizing delivered waste capacity. It incorporates several modifications to the Genetic Algorithm (GA) such as adding distance minimization, energy conservation and capacity maximization mechanisms in the random initial generation, customizing the fitness function and using a hybrid space search in the mutation. The GA is well-designed to find feasible solutions while considering examining three quantities of waste in collection locations produced from synthetic data. Experimental results show that the proposed approach outperforms other previous research approaches addressing the same constraints and with a similar dataset. It is noteworthy that the proposed approach achieved an average reduction in errors in 100 instances compared to previous research by more than 81%. Whereas for 500 instances, the reduction in error reached more than 59%, while in1000 instances, the error was reduced by more than 44%. Those promising results proved that GA utilization can improve the waste collection process through identifying the best appropriate tours that can minimize the cost via distance minimization, energy conservation and capacity maximization mechanisms. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the University of Bahrain.

关键词： Genetic algorithm (GA) multi-objective constraints path planning unmanned aerial vehicles (UAV) waste collection waste management system

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：