Hybrid of artificial immune system and particle swarm optimization-based support vector machine for Radio Frequency Identification-based positioning system

This study intends to propose a hybrid of artificial immune system (AIS) and particle swarm optimization (PSO)-based support vector machine (SVM) (HIP–SVM) for optimizing SVM parameters, and applied it to radio frequency identification (RFID)-based positioning system. In order to evaluate HIP–SVM’s capability, six benchmark data sets, Australian, Heart disease, Iris, Ionosphere, Sonar and Vowel, were employed. The computational results showed that HIP–SVM has better performance than AIS-based SVM and PSO-based SVM. HIP–SVM was also applied to classify RSSI for indoor positioning. The experiment results indicated that HIP–SVM can achieve highest accuracy compared to those of AIS–SVM and PSO–SVM. It demonstrated that RFID can be used for storing information and in indoor positioning without additional cost.

► Propose a hybrid of AIS and PSO-based SVM (HIP–SVM) for optimizing SVM parameters.
► Apply HIPSVM to RFID-based positioning system.
► The results showed that HIP–SVM has better performance than AIS and PSO.