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检索条件"主题词=Ant Colony optimization algorithm"

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The Research of Topology Control in Wireless Sensor Network Based on The Monitoring of Transmission and Distribution Lines

The Research of Topology Control in Wireless Sensor Network ...

引用

International Conference on Mechatronics and Semiconductor Materials (ICMSCM 2013)

作者： Jia Huibin Zhang Caixia North China Elect Power Univ Baoding Peoples R China

ISBN: (纸本)9783037859391

For the performance of wireless sensor network, topology control plays an important role. In order to save energy, prolong the lifetime of the network and reduce the interference between nodes, while ensuring the connectivity and reliability of data transmission, we propose a suitable scheme which is hierarchical topology control with distributed and a long- chain structure based on the monitoring of transmission and distribution lines. It is based on the study of the monitoring of transmission and distribution lines in wireless sensor *** scheme will cluster with the deployed nodes, and fuse with data in the selected cluster head,in order to complete topology *** matlab optimization platform, using ant colony optimization algorithm to remove some redundant links, so that data transmit to the monitoring center reliably and complete real-time monitoring of the lines,thus prolong the lifetime of the network.

关键词： topology control clustering ant colony optimization algorithm wireless sensor network transmission and distribution lines

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Multi-Stage Partner Selection Based on Genetic-ant colony algorithm in Agile Supply Chain Network

Multi-Stage Partner Selection Based on Genetic-Ant Colony Al...

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9th International Conference for Young Computer Scientists

作者： Lin, Zheng Wang, Lubin Cent Univ Finance & Econ Beijing Peoples R China

ISBN: (纸本)9781424441983

It is shown that partner selection in agile supply chain network can be represented as a directed graph model. This paper considers multi-stages in this process instead of one. The natures of the proposed model enable us to design a meta-heuristic algorithm -- GACA which combined the pros part of ACOA and GA for solving the good path search problem. Computational results show the efficiency of our algorithm in compare to the other techniques.

关键词： Partner selection genetic algorithm ant colony optimization algorithm agile supply chain network

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Wavelet Threshold Denoising of ACO Optical Lens Image 1st

Wavelet Threshold Denoising of ACO Optical Lens Image

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1st European-Alliance-for-Innovation (EAI) International Conference on Advanced Hybrid Information Processing (ADHIB)

作者： Xue, Ping Niu, Xiangyong Zhu, Xiaohui Wang, Hongmin Chen, Jihua Harbin Univ Sci & Technol Sch Automat Harbin 150080 Heilongjiang Peoples R China

ISBN: (纸本)9783319733173;9783319733166

In the system of the image defect detection system, during image acquisition and transmission, the salt-and-pepper Noise will adversely affect the subsequent processing and recognition. To eliminate the salt-and-pepper noise effectively, a defect image denoising algorithm based on ant colony optimization wavelet threshold is improved in this paper. Firstly, the basic principle of wavelet denoising is analyzed theoretically, and a compromise threshold function and a GCV optimal threshold selection method are adopted. It uses ant colony algorithm to optimize the wavelet threshold, which greatly improves the speed and accuracy of the optimal threshold. Using standard soft threshold method, GCV threshold optimization method and the ant colony optimization wavelet threshold method, the defect image of the lens is denoised. The results of experiment indicate that the algorithm can remove the salt-and-pepper noise in the image of defective lenses more effectively than the other two algorithms, and improve the accuracy of the lens detection. This algorithm is also suitable for general image denoising.

关键词： Lens defect image Wavelet denoising ant colony optimization algorithm Salt-and-pepper noise

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Architecture Design and Implementation of UUV Mission Planning 40

Architecture Design and Implementation of UUV Mission Planni...

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40th Chinese Control Conference (CCC)

作者： Wei, Dunwen Hao, Pengfeng Ma, Hongjiao Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9789881563804

With the rapidly increasing demand for unmanned underwater vehicles (UUVs) application, UUVs have been widely implemented to explore marine resources and perform marine missions. Robust software architecture is a fundamental guarantee to achieve autonomous mission decision-making for UUVs in the complex ocean environment. In this paper, we proposed one software architecture of UUVs for complex multi-mission decision making with the mission planning and mission replanning functions. This software architecture can make a rapid response to the changes in mission quantity, attributes, and so on. Besides, real-time mission re-planning is possible. Considering that multi-mission decision is an NP optimal problem, we improved our previous mission planning model and used the metaheuristic algorithm, ant colony optimization algorithm (ACO), to prove the correctness and effectiveness of software architecture, and the performance of different missions operations are compared by the experimental simulations.

关键词： Unmanned underwater vehicles(UUVs) software architectures mission planning mission re-planning metaheuristic algorithm ant colony optimization algorithm

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Task Offloading Scheduling in Mobile Edge Computing Networks

Task Offloading Scheduling in Mobile Edge Computing Networks

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12th International Conference on Ambient Systems, Networks and Technologies (ant) / 4th International Conference on Emerging Data and Industry 4.0 (EDI40)

作者： Wang, Zhonglun Li, Peifeng Shen, Shuai Yang, Kun Univ Elect Sci & Technol China Quzhou Res Inst Quzhou Zhejiang Peoples R China Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

In this paper, we investigate the task scheduling in resource-limited mobile edge computing (MEC) network, where multiple base stations (BSs), each equipped with a MEC server, assist multiple latency-sensitive user equipments (UEs) in computing. We aim to jointly minimize the system energy consumption and maximize the number of offloaded tasks by optimizing the task scheduling between UEs and BSs. A multiple-objective and mix-integer problem is formulated, which is difficult to solve. To tackle the problem, we combine the ant colony optimization (ACO) algorithm with load balancing, and propose an efficient algorithm. The simulation demonstrates the effectiveness of the proposed algorithm. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Energy minimization offloading number maximization mobile edge computing load balancing ant colony optimization algorithm

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Path planning research based on the improved ant colony algorithm in ECDIS 35

Path planning research based on the improved ant colony algo...

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35th Chinese Control Conference (CCC)

作者： Meng, Hao He, Xiaopeng Song, Jingguo Liu, Zhilin Harbin Engn Univ Coll Automat Harbin 150001 Peoples R China

ISBN: (纸本)9789881563910

ECDIS (Electronic Chart Display and Information System) is a real-time navigation system, which integrates the chart display, navigation operations, route calculation and navigation marks, etc. and provides a great convenience for maritime navigation. Path planning in ECDIS system plays a significant role. Based on the principle of intelligent ant colony algorithm, and in view of the ant colony algorithm is slow convergence speed, easy to fall into local optimal solution, the article uses adaptive pseudo random selection rules, dynamically adjusts volatilization factor, updates the optimal route of ants pheromone, so as to improve the convergence speed of path planning and the effect of algorithm to achieve the goal of the optimal route. Simulation results validate the feasibility of the improved ant colony algorithm and its effectiveness.

关键词： ECDIS ant colony optimization algorithm Path planning Optimal route

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System Management Model Based on ABC-ACO algorithm under the Background of Digital Economy 1

System Management Model Based on ABC-ACO Algorithm under the...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Fu, Yong College of Economics Sichuan Agricultural University Sichuan Chengdu China

ISBN: (纸本)9798350316469

With the rapid development of the digital economy, the need to manage complex systems is increasing day by day, and traditional management methods are often unable to fully adapt to this challenge. Therefore, this research proposes a system management model based on the ABC-ACO algorithm, which aims to optimize decision-making and resource allocation and improve system performance and efficiency. In this paper, the ABC-ACO algorithm is used as the main optimization tool. The algorithm combines artificial bee colony algorithm (ABC) and ant colony optimization algorithm (ACO), and has the characteristics of adaptive search and iterative optimization. Through experimental tests, the accuracy rate of the algorithm in this paper is 92% at the highest and 86% at the lowest. The system management model based on the ABC- ACO algorithm performs well in the context of the digital economy. By optimizing decision-making and resource allocation, the model can improve the performance and efficiency of the system. © 2023 IEEE.

关键词： ABC-AOC algorithm ant colony optimization algorithm Artificial Bee colony algorithm Digital Economy

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Learning ant colony algorithm for Solving Green Multi-compartment Vehicle Routing Problem with Simultaneous Pickup and Delivery 7

Learning Ant Colony Algorithm for Solving Green Multi-compar...

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7th International Conference on Automation Electronics and Electrical Engineering

作者： Geng, Yan Guo, Ning Bin Qian Kunming Univ Sci & Technol Informat Engn & Automat Inst Kunming Yunnan Peoples R China

ISBN: (纸本)9798350377040;9798350377033

To solve the Green Multi-Compartment Vehicle Routing Problem with Simultaneous Pickup and Delivery (GMCVRPSPD), a mathematical model is established with the objective of minimizing total costs. The model consists of vehicle operating costs and fuel consumption costs. A Learning ant colony optimization algorithm (LACO) is proposed to solve GMCVRPSPD. Firstly, in the global search stage of LACO, three different heuristic methods are used to generate the initial solution. These solutions are used to initialize the pheromone matrix, providing a better starting point for the algorithm's subsequent search process. A pheromone update mechanism is designed to effectively enhance the convergence speed of the algorithm. Secondly, in the local search of LACO: a local learning strategy is designed, which selects the neighborhood operation suitable for the current search stage from the six neighborhood operations designed, and dynamically updates the selection probability value according to the contribution value generated by the neighborhood operation. In addition, a rapid evaluation strategy is designed to reduce the calculation time in the local search process. Finally, the effectiveness of LACO in solving GMCVRPSPD problem is verified by simulation experiment and algorithm comparison.

关键词： Multi-Compartment Vehicle Routing Problem Simultaneous Pickup and Delivery Green Learning Mechanism ant colony optimization algorithm

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Study on the application of simulated evolutionary optimization algorithm in optimizing the combination of unit commitments in power system

Study on the application of simulated evolutionary optimizat...

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3rd International Conference on Measuring Technology and Mechatronics Automation (ICMTMA 2011)

作者： Liu Qing-song Tong Jia Li Yi-feng Jiaxing Univ Jiaxing 314001 Zhejiang Peoples R China Guangdong Ocean Univ Zhanjiang Guangdong 524088 Peoples R China

ISBN: (纸本)9783037850190

This study adopted a simulated evolutionary optimization algorithm, ant colony optimization algorithm to find the optimal unit commitment operation. The concepts such as status, strategy, and path, etc. were introduced to devise the optimization of unit commitment operation by ant colony optimization algorithm mode, so that the optimal unit commitment operation could be found by ant colony optimization algorithm. To cope with different constraints by additional penalties and restrict the statuses not satisfying the constraints by tabu table, the retrieval of ant colony optimization algorithm could always be performed in feasible region and the retrieval process of the algorithm was effectively conducted. It is feasible and efficient to find the optimal unit commitment operation by ant colony optimization algorithm, which was proved by stimulation.

关键词： Power system Optimal operation Optimal unit commitment operation Evolutionary optimization algorithm ant colony optimization algorithm

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A Network-Assisted Approach for Ransomware Detection

A Network-Assisted Approach for Ransomware Detection

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作者： Xia, Tianrou PennState University Libraries

学位级别：Master of Science

Ransomware is one kind of malware using cryptography to prevent victims from normal use of their computers. As a result, victims lose the access to their files and desktops unless they pay the ransom to the attackers. By the end of 2019, ransomware attack had caused more than 10 billion dollars of financial loss to enterprises and individuals. In this work, we propose a Network-Assisted Approach (NAA), which contains local detection and network-level detection, to help user determine whether a machine has been infected by ransomware. To evaluate its performance, we built 100 containers in Docker to simulate network scenarios. A hybrid ransomware sample which is close to real-world ransomware is deployed on stimulative infected machines. The experiment results show that our network-level detection mechanisms are separately applicable to WAN and LAN scenarios for ransomware detection.

关键词： Ransomware Detection network security ant colony optimization algorithm

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