Simultaneous Feature Selection and Support Vector Machine Optimization Using an Enhanced Chimp Optimization Algorithm

Chimp Optimization Algorithm (ChOA), a novel meta-heuristic algorithm, has been proposed in recent years. It divides the population into four different levels for purpose of hunting. However, there are still some defects that lead to algorithm falling local optimum. To overcome these defects, an Enhanced (EChOA) is developed this paper. Highly Disruptive Polynomial Mutation (HDPM) introduced further explore space and increase diversity. Then, Spearman’s rank correlation coefficient between chimps with highest fitness lowest calculated. In order avoid optimization, low values Beetle Antenna Search (BAS) obtain visual ability. Through introduction above three strategies, ability exploration exploitation enhanced. On basis, paper proposes EChOA-SVM model, which can optimize parameters while selecting features. Thus, maximum classification accuracy be achieved as few features possible. verify effectiveness method, method compared seven common methods, including original algorithm. Seventeen benchmark datasets from UCI machine learning library used evaluate accuracy, number features, methods. Experimental results show better than other methods on most data sets, required by also less algorithms.