A differential evolution algorithm combined with cloud model for RFID reader deployment

Abstract

Differential Evolution (DE) is a numerical optimization approach, which is simple to implement, requires little parameter tuning, and known for remarkable performance. It mainly uses the distance and direction information from the current population to guide its further search. However, it has no mechanism to extract and use global information about the search space. Cloud model is an effective tool in uncertain transforming between qualitative concepts and their quantitative expressions. It can be used to extract the global statistical information about the search space. In this paper, we introduce a new optimization algorithm combining differential evolution and cloud model. The innovation of the algorithm is extraction of the global statistical information about the search space and production of new solutions according to the cloud model. The best individuals from the current population are used to build cloud digital characteristics of good solution regions by backward cloud generator. And then these cloud digital characteristics are used to produce new individuals by positive cloud generator. Both the local information from DE and global information from cloud model are used to guide the further search. The proposed algorithm is applied to some well-known benchmarks. The relative experimental results show that the algorithm has stronger global search ability than original version of DE. Finally, an application of the algorithm to RFID sensor deployment is presented.