Research on Genetic Algorithm of Network Search under Computer Artificial Intelligence Big Data Technology

In this paper, an improved genetic simulated annealing algorithm (SAGA) for wireless sensor networks is studied and designed. The aim of this project is to better study the optimal transmission path in wireless sensor networks (WSN s) based on inter-node communication and reduce node energy consumpt...

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Bibliographic Details
Published in2024 4th Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) pp. 675 - 679
Main Authors Li, Yuan, Yu, Xin
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.02.2024
Subjects
Approximation algorithms
artificial intelligence
big data technology
energy consumption
genetic algorithm
Genetics
Optimal control
Search problems
Simulated annealing
simulated annealing algorithm
Wireless communication
Wireless sensor network
Wireless sensor networks
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DOI10.1109/ACCTCS61748.2024.00125

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Summary:In this paper, an improved genetic simulated annealing algorithm (SAGA) for wireless sensor networks is studied and designed. The aim of this project is to better study the optimal transmission path in wireless sensor networks (WSN s) based on inter-node communication and reduce node energy consumption. Firstly, the model is built based on the optimal control index. Then the fitness function, cross operation and variation operation in the evolutionary process are studied. The routing calculation and genetic evolution calculation are carried out at the same time until the approximate optimal path is found. This method can solve the traditional local optimization problem well. Then the Metropolis criterion is given to make the jump variable in the simulated annealing algorithm have some regularity. Simulation results show that the proposed algorithm can effectively solve the problem of wireless sensor path optimization. Compared with the existing methods, the proposed method not only saves a lot of energy, but also saves a lot of calculation time.
DOI:10.1109/ACCTCS61748.2024.00125