There are the disadvantages of easily falling into premature convergence and local optimal solution which the elementary artificial bee colony algorithm had in some degree.Chaotic Catfish effect was hence adopted in this paper to achieve the optimum performance of artificial bee colony algorithm

in which

chaotic mechanism was conducted to instantiate each individual of the swarm firstly owing to its marvelous intrinsic randomness.Then the efficacious competition and coordination mechanism among Catfish bees which were derived from the integration of Chaos theory with Catfish effect and originals were intended to boost the capabilities of them leaping out of local optimal solution and c

onverging expeditiously.The practicability of Support Vector Machines(SVM)is excessively affected due to the difficulty of selecting appropriate penalty factor

C

and kernel function parameter of SVM.Conversely

all of the common SVM parameters optimization methods have their respective disadvantages with some degree of competence.We utilized the improved artificial bee colony algorithm to optimize the two parameters of SVM

simultaneously

the public datasets from the University of California-Irvine(UCI)and the activity recognition reality data were employed for evaluating the proposed model.Experimental results demonstrate that the classification accuracy obtained by the developed SVM was higher