Dynamic Path Optimization Based on Improved Ant Colony Algorithm

Cheng, Juan

doi:10.1155/2023/7651100

Cited by 10 publications

(4 citation statements)

References 18 publications

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“…The experimental results showed that the running time of the optimal path obtained by this algorithm was obvious. In [33], an ant colony optimization algorithm based on the small-window strategy was proposed to solve the routing and wavelength allocation problems in satellite optical networks. The results showed that, compared with Dijkstra's algorithm, this algorithm improved the system resource utilization by 45%.…”

Section: Related Researchmentioning

confidence: 99%

Satellite Network Security Routing Technology Based on Deep Learning and Trust Management

Liu,

Rong,

Jiang

et al. 2023

Sensors

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The conventional trust model employed in satellite network security routing algorithms exhibits limited accuracy in detecting malicious nodes and lacks adaptability when confronted with unknown attacks. To address this challenge, this paper introduces a secure satellite network routing technology founded on deep learning and trust management. The approach embraces the concept of distributed trust management, resulting in all satellite nodes in this paper being equipped with trust management and anomaly detection modules for assessing the security of neighboring nodes. In a more detailed breakdown, this technology commences by preprocessing the communication behavior of satellite network nodes using D–S evidence theory, effectively mitigating interference factors encountered during the training of VAE modules. Following this preprocessing step, the trust vector, which has undergone prior processing, is input into the VAE module. Once the VAE module’s training is completed, the satellite network can assess safety factors by employing the safety module during the collection of trust evidence. Ultimately, these security factors can be integrated with the pheromone component within the ant colony algorithm to guide the ants in discovering pathways. Simulation results substantiate that the proposed satellite network secure routing algorithm effectively counters the impact of malicious nodes on data transmission within the network. When compared to the traditional trust management model of satellite network secure routing algorithms, the algorithm demonstrates enhancements in average end-to-end delay, packet loss rate, and throughput.

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Section: Related Researchmentioning

confidence: 99%

Satellite Network Security Routing Technology Based on Deep Learning and Trust Management

Liu,

Rong,

Jiang

et al. 2023

Sensors

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“…Currently, enterprises are facing greater competitive pressure for survival, and they are placing greater emphasis on optimizing distribution issues, reducing costs, and enhancing competitiveness [13,14]. The progress of science and technology can effectively solve this problem [15][16][17][18]. Adopting advanced technological means and methods, such as the ant colony algorithm discussed in this article, as well as other technologies such as particle swarm optimization and genetic algorithm, can achieve the goal of optimizing distribution paths and fundamentally improve the efficiency of logistics systems [19,20].…”

Section: Introductionmentioning

confidence: 99%

Logistics Delivery Path of S Electronics Company Based on Ant Colony Algorithm

Jiang,

Zheng,

et al. 2024

Advances in Transdisciplinary Engineering

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Optimizing logistics delivery paths using artificial intelligence technology is an important way for enterprises to achieve cost reduction and efficiency increase. Based on the analysis and research of several main distribution path optimization methods, and based on the characteristics of each method, the article chooses to use ant colony algorithm to carry out the research of this paper. This paper expounds the principle and algorithm process of the ant colony algorithm and based on the actual problems and data of S Electronics’ logistics distribution, conducts modeling research on its logistics distribution path, and then compares and analyzes the results of its empirical research with those under the traditional algorithm. The conclusion is that although the total distance of the ant colony algorithm is slightly increased compared to traditional algorithms, it can save one transportation vehicle and its labor costs for enterprises, and help enterprises greatly save delivery costs. Compared to traditional algorithms, the ant colony algorithm has better performance.

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“…Researchers have widely applied Bionic Algorithms to resolve the issue of autonomous path planning for UAVs. Bionic algorithms include particle swarm optimization (PSO) 1 – 3 , wolf swarm optimization (WSO) 4 – 7 , and ant colony optimization (ACO) 8 . Although these algorithms exhibit strong robustness and adaptability, they are susceptible to outcomes that are locally optimal 9 .…”

Section: Introductionmentioning

confidence: 99%

Research on obstacle avoidance path planning of UAV in complex environments based on improved Bézier curve

Zhang,

Liu,

Feng

2023

Sci Rep

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Obstacle avoidance path planning is considered an essential requirement for unmanned aerial vehicle (UAV) to reach its designated mission area and perform its tasks. This study established a motion model and obstacle threat model for UAVs, and defined the cost coefficients for evading and crossing threat areas. To solve the problem of obstacle avoidance path planning with full coverage of threats, the cost coefficients were incorporated into the objective optimization function and solved by a combination of Sequential Quadratic Programming and Nonlinear Programming Solver. The problem of path planning under threat full coverage with no solution was resolved by improving the Bézier curve algorithm. By introducing the dynamic threat velocity obstacle model and calculating the relative and absolute collision cones, a path planning algorithm under multiple dynamic threats was proposed to solve the difficulties of dynamic obstacle prediction and avoidance. Simulation results revealed that the proposed Through-out method was more effective in handling full threat coverage and dynamic threats than traditional path planning methods namely, Detour or Cross Gaps. Our study offers valuable insights into autonomous path planning for UAVs that operate under complex threat conditions. This work is anticipated to contribute to the future development of more advanced and intelligent UAV systems.

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Dynamic Path Optimization Based on Improved Ant Colony Algorithm

Cited by 10 publications

References 18 publications

Satellite Network Security Routing Technology Based on Deep Learning and Trust Management

Satellite Network Security Routing Technology Based on Deep Learning and Trust Management

Logistics Delivery Path of S Electronics Company Based on Ant Colony Algorithm

Research on obstacle avoidance path planning of UAV in complex environments based on improved Bézier curve

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