Cloud-Based Adaptive Particle Swarm Optimization for Waveband Selection in Big Data

In the process of infrared spectrum analysis, wavebands selections is very important to deduce the dimension of spectrum data and improve the accuracy of analysis model. There are some methods are used in wavebands selections, such asGenetic Algorithm (GA) andParticle Swarm Optimization (PSO), variance analysis method, correlation coefficient method, Uninformative Variables Elimination (UVE) method, interval Partial Least Squares (iPLS) method, stepwise regression method, etc. But most of these methods are used in nearly infrared spectrum analysis, and are not good enough in waveband selection. In this paper, Cloud-Based Adaptive Particle Swarm Optimization (CAPSO) algorithm is introduced to select the waveband. Its optimization process is based on cloud theory in the fuzzy control field. According to the prior computation result, it can adjust the current evolution strategy using three ways. Due to this strategy, CAPSO algorithm can reduce the number of particle easily and the convergence speed is faster than the other compared algorithms. Experiments showed that CAPSO algorithm has clear advantage in optimization time, the dimension of the selected spectrum data and the accuracy of the analysis model compared with PSO and GA.