Network node selection method and system based on whale optimization algorithm and storage medium

Abstract

The invention discloses a network node selection method and system based on a whale optimization algorithm, and a storage medium. The method comprises the following steps: setting a binary coded population matrix, setting the maximum number of iterations and the initial number of iterations, and randomly initializing the population matrix; calculating the target function according to the node selection scheme corresponding to each whale individual in the population to obtain the optimal individual position and the optimal fitness function value in the population; calculating a current dynamicconvergence factor and a current dynamic weight according to the current number of iterations; determining a scheme for calculating the whale individual position according to the current dynamic convergence factor and the generated random number, and updating the current whale individual position and the number of iterations; if the number of iterations reaches the maximum number of iterations, returning to the optimal whale individual position, and determining sensor nodes participating in tracking according to the optimal whale individual position; otherwise return computation. According tothe invention, the tracking precision and the real-time performance in the target tracking process in the wireless sensor network can be improved.

Description

technical field [0001] The invention relates to the technical field of target tracking, in particular to a network node selection method, system and storage medium based on a whale optimization algorithm. Background technique [0002] With the development of technology in the field of microelectronics, wireless sensor networks are gradually widely used in military, environmental monitoring and many other fields, and target tracking is also one of the important application scenarios of wireless sensor networks. In the actual application process, using all sensor nodes to participate in target tracking can obtain good tracking accuracy, but due to the limitation of the sensor's own energy, using all sensor nodes to participate in tracking will greatly reduce the network life. How to ensure the accuracy of target tracking without reducing the life of the sensor network is a problem that needs to be solved urgently in the field of target tracking. Node selection can solve this ...

AI Technical Summary

Problems solved by technology

[0003] At present, the algorithms used for node selection problems include genetic algorithm, particle swarm optimization algorithm, convex optimization algorithm, etc., but these methods usually have the characteristics of low tracking accuracy, slow convergence speed, and easy to fall into local optimum.

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