Metaheuristic link prediction (MLP) using AI based ACO-GA optimization model for solving vehicle routing problem

Revanna, Jai Keerthy Chowlur; Al-Nakash, Nushwan Yousif B.

doi:10.1007/s41870-023-01378-5

Cited by 5 publications

(1 citation statement)

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“…The least outage path for a particular routing session was chosen using the pattern aware-minimum outage intelligent cooperative routing (SA-MOICR) algorithm. An artificial intelligence system to improve the energy and pattern aware clustering model, which uses a genetic algorithm for vehicular routing, was proposed in paper [21] as a means of improving the routing protocol. This will enhance the routing protocol's performance.…”

Section: Related Workmentioning

confidence: 99%

Chunk Integrated Network Slicing(CINS) with Eagle Sight View Optimization for 5G System using wireless networks

Rao,

Gaur

2024

Preprint

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The use of high-speed transmission media is improved by the development of data communication systems. The network strength and data routing determine this speed. Because of this, the 5G network architecture increased bandwidth use and improved the LTE model, which improved communication system performance. Since these parameters are insufficient to obtain the maximum level of data transmission speed increase. The ideal routing system is the subject of the majority of research studies. An improved version of Seagull with Eagle Sight View Optimization (SESVO) is used in this suggested work in order to boost 5G system performance. In order to slice the network into multiple combinations, the optimization algorithm chooses the optimum network slicing strategy. This determines the best availability path to route the data with the best distance prediction and quick switching effect after ideally analysing the traffic level in a network path. The suggested method calculates the correlation coefficient between the communication nodes and weights the dynamic traffic that flows through the routing path. The optimization approach uses these estimated properties to choose the best attributes from the node arrangement's total feature set. By reusing the link pattern, the SESVO algorithm predicts relative feature qualities and develops a clustering architecture to depict high data transfer. The optimal routing path is represented by the convergence graph's best fitness value at each iteration of the optimization method. It is possible to analyse the optimal routing path and pattern availability for a node at any given time by looking at the SESVO objective function's fitness. The experimental result justifies the efficiency of proposed work comparing to other state-of-art methods for routing and link allocation system.

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Section: Related Workmentioning

confidence: 99%