关键词: Levy flight, 自适应, 粒子群优化, 红外气体传感器, 温度补偿

Abstract: Focused on the issue that the precision of infrared gas sensor is affected greatly by temperature, a new method is put forward for sensor temperature compensation based on Adaptive Levy mutation Immune Particle Swarm Optimization-
Least Square Support Vector Machine（ALIPSO-LSSVM）. Levy flight is introduced in the adaptive mutation of offspring to ensure the diversity, and opposition-based learning is used to initialize the particle swarm to improve the convergence speed in the ALIPSO algorithm. Performance comparison with other PSOs is made through 5 benchmark test functions. Based on the ALIPSO, the optimum parameter selection of Least Squares SVM（LS-SVM） is studied, and the temperature compensation model of infrared gas sensor is established, the numerical simulation results show the relative error can be controlled within 6%.

Key words: Levy flight, adaptive, particle swarm optimization, infrared gas sensor, temperature compensation