Optimal Placement of Laying Hen House Temperature Sensors Based on Genetic Algorithm

Abstract

The arrangement of temperature sensors in most existing large-scale laying hen houses is random or placed according to the experience of breeders. However, this can’t achieve accurate monitoring of the henhouse. The temperature in the laying hen houses cannot be uniformly controlled, leads to the reduction of production efficiency of laying hens. In this paper, aiming at the placement of temperature sensors in laying hens’ houses, a placement optimization method was proposed. Firstly, the correlation coefficient of sensors is calculated to eliminate redundant sensors. Then, all the remaining sensors are arranged and combined. Finally, taking the grey correlation degree of each combination as the objective function, the dual-structure coding genetic algorithm is used to optimize the sensor combination. The strategy was tested in a large hen house. When the initial deployment of 81 sensors is reduced to at least 3, the established target can still be achieved, and the position of the target sensor can be calculated. This strategy not only meets the goal of accurately monitoring the hen house temperature, but also saves the hardware cost, which has important application value.

Keywords

• Dual-structure coding
• genetic algorithm
• grey correlation degree
• large laying hen house
• optimal sensor placement