Study On Immune Hybird Algorithm And Application On Data Mining And Optimization

Artificial immune system (AIS) was developed based on biological immune system which is a highly parallel, distributed, adaptive and self-organizing system, and has a strong learning, recognition, memory,,and feature extraction capabilities. In this dissertation an immune memory strategy was used to make up the weakness of PSO. Then a framework for algorithm designing was discussed. Furthermore a shortcoming of immune optimization algorithm was studied, and an effective solution was proposed. The main contributions of this dissertation are described as follows:Firstly, classification problem was discussed. Due to the scale of target data for classification task was growing larger, a incremental learning technology was required. In order to make up the drawbacks of PSO in incremental learning abilities, the AIRS memory strategy was injected, and a hybird algorithm--AIR-PSO was presentd. Later AIR-PSO was tested on a number of UCI standard data sets. It showed that the hybrid algorithm had the ability of incremental learning, and also had the advantage in classification accuracy.Secondly, a general framework for immune clustering algorithm was discussed. Based on a depth study on aiNet process, a general framework for immune clustering algorithms was proposed. The framework was build up by five parts, and each part adopted a lot of crucial methods. The framework gave a systematic guidance for the design of new algorithms.Finally, the immune optimization algorithm was studied. Opt-aiNet algorithm encountered a lower convergence rate in single objective problems sometimes. Studies have shown that the random mutation of the population may lead to this problem. Then an oriented mutaion process inspired by PSO evolution strategies was proposed, and a bi-mutaion opt-aiNet was raised up. After that the bi-mutaion opt-aiNet was applied to the wireless network planning, and achieved a better result than GA and the traditional opt-aiNet.