A projection pursuit dynamic cluster model based on a memetic algorithm

Abstract

A Projection Pursuit Dynamic Cluster (PPDC) model optimized by Memetic Algorithm (MA) was proposed to solve the practical problems of nonlinearity and high dimensions of sample data, which appear in the context of evaluation or prediction in complex systems. Projection pursuit theory was used to determine the optimal projection direction; then dynamic clusters and minimal total distance within clusters (min TDc) were used to build a PPDC model. 17 agronomic traits of 19 tomato varieties were evaluated by a PPDC model. The projection direction was optimized by Simulated Annealing (SA) algorithm, Particle Swarm Optimization (PSO), and MA. A PPDC model, based on an MA, avoids the problem of parameter calibration in Projection Pursuit Cluster (PPC) models. Its final results can be output directly, making the cluster results objective and definite. The calculation results show that a PPDC model based on an MA can solve the practical difficulties of nonlinearity and high dimensionality of sample data.