Multi-channel Transmission Optimization of Campus Internet of Things Based on Pheromone Genetic Algorithm

doi:10.16182/j.issn1004731x.joss.18-0733

Abstract

Abstract: Aiming at the difficulty in selecting and optimizing the multi-path transmission of campus Internet of Things information, an optimization algorithm of network multi-path transmission based on intelligent optimization algorithm is proposed. Based on the standard genetic algorithm and the concept of pheromone concentration in ant colony algorithm, this algorithm improves the global optimization ability and convergence efficiency by controlling the evolution direction of individuals, and designs and constructs an evaluation index mathematical model which conforms to the characteristics of multi-channel information transmission optimization in the Internet of Things. The mathematical model of the evaluation index realizes the multi-channel comprehensive scoring based on the entropy weight ideal point method.Finally the optimal network path that meets the engineering requirements is obtained through multi-generation evolution. The simulation results show that the pheromone genetic algorithm has stronger global optimization ability and faster convergence speed than the standard genetic algorithm, which provides a feasible solution for the multiplex optimization problem of the campus IoT information .

Key words: genetic algorithm, pheromone, internet of things, path optimization

CLC Number:

TP391.9

Chen Zhiyong, Liu Hao. Multi-channel Transmission Optimization of Campus Internet of Things Based on Pheromone Genetic Algorithm[J]. Journal of System Simulation, 2019, 31(8): 1719-1726.

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