Traditional sensor array optimization usually uses single objective optimization

which ignores the influence of other important factors.A multi-objective optimization research method for sensor array based on genetic algorithm quantum behavior particle swarm optimization（GA-QPSO）is proposed.The concept of information entropy is used to construct two objective functions of the sensor.The crossover and mutation operations of genetic algorithm（GA）are introduced into the quantum behavior particle swarm optimization（QPSO）

and the mechanism of self-adaptive updating of the probability of the two is adopted.The algorithm is used to find the non-dominated solution set and the corresponding optimal sensor combination.The experimental results show that the algorithm can find the optimal combination set under different array sizes

and reduce the size of the original array.In addition

compared with single objective optimization

the algorithm based on multi-objective optimization has better classification precision.The optimized sensor array can ensure better input quality.