Investigation of differential evolution and particle swarm optimization in search performance

Abstract

The purpose of this study is to clarify the search performance of differential evolution (DE) and particle swarm optimization (PSO) technologies for instinctively understanding the specificity of the used search methods. Due to achieve the task, here, the several search methods of both, i.e. DE/rand/1, DE/rand/2, DE/best/1, DE/best/2, the PSO, PSOIW, and CPSO, which are implemented in this paper. Therefore, many computer experiments are carried out for handling the given four benchmark problems. Through the analysis of the obtained experimental data, the detail search performance and characteristics of them are observed and compared, respectively. From the obtained results, it is found that the search methods of DE/best/1 and the PSO relatively have better search performance. Based on the findings and know-how, they can provide some important reference and key hint for encouraging development and improvement of both DE and PSO technologies in the near future. And as the applicative examples, the PSO is used to handle typical 2-bit and 3-bit parity problems for pattern classification.