Grasshopper optimization algorithm based on chaos and cauchy mutation and feature selection

There are some shortages when traditional grasshopper optimization algorithm deals with some complicated optimization problems, such as slower convergence speed and more easy to fall into a local optimum. For this reason, a non-linear grasshopper optimization algorithm CCGOA, which combines chaotic mapping and Cauchy mutation mechanism, is proposed. The population is initialized by mixing chaotic Tent map and opposite-learning mechanism, which can ensure better-quality individuals in initial population and make all individuals distributed more uniform in search space as far as possible. A non-linear adaptive coefficient update mechanism based on cosine function is designed to get better trade-off between global search and local development. And, Cauchy mutation is introduced to mutate and disturb the current optimal individual to avoid the algorithm falling into the local optimum. The benchmark function optimization test proves that the algorithm can effectively improve the optimization accuracy and convergence speed. The feature selection algorithm CCGOA-FS is designed and applied to solve the feature selection problem. Several data set tests proved that the algorithm can effectively select the optimal feature subset and improve the accuracy of data classification.