关键词: 细菌觅食算法（BFA）, 参数优化, 支持向量机（SVM）

Abstract: For original Bacterial Foraging Algorithm（BFA） exists the problems of slow convergence and large amount of calculation, firstly, by improving bacterial population size, movement step length and introducing the iterative termination conditions to improve the original BFA, and then applies it to Support Vector Machine（SVM） parameters optimization. Based on Iris standard test data sets, experiments take the penalty factor [C] and kernel parameter [γ]of Gaussian kernel SVM as the optimization objects, and analyze the optimization performance of genetic algorithm, particle swarm algorithm, original and improved BFA, the results verify that applied the improved bacterial foraging algorithm in SVM parameter optimization has superiority.

Key words: Bacterial Foraging Algorithm（BFA）, parameters optimization, Support Vector Machine（SVM）