Sensor array optimization is one of the methods to enhance the performance of electronic noses

which uses the feature selection to decide the right kinds and number of gas sensors.Generally there are two different methods for feature selection

searching method and non-searching method.Four searching strategies are adopted in a flammable liquids detection experiment

which are genetic algorithm（GA）

simulated-annealing algorithm

generalized sequential forward selection and random searching algorithm

and their performances are compared.Five kinds of class separative criterion are compared here as well.Results show that generalized sequential forward selection possesses excellent optimization property and spended the least time in this experiment

thus having higher efficiency than other three methods.And the criterion J3 based on class distance can estimate the class separability relatively appropriately.