An Extended Particle Swarm Optimizer （EPSO） is proposed in this paper. In this new algorithm, not only the local but also the global best position will impact the particle＇s velocity updating process. EPSO is an integration of Local Best paradigm （LBEST） and Global Best paradigm （GBEST） and it significantly enhances the performance of the conventional particle swarm optimizers. The experiment results have proved that EPSO deserves to be investigated.
To solve the problems in knowledge management system （KMS）, such as information sharing, the ability to extend and re-engineer, and the reusable ability of legacy systems in distributed and heterogeneous environments. This article presents a method based on agent and ontology of designing KMS. This method consists of two agencies. One is knowledge agency with three agents supporting knowledge management process. The other is application agency with three agents supporting knowledge application. In this method, ontology is used to represent the knowledge in knowledge base and the content in the message exchanged among agents. To demonstrate the advantages of this method, experiments have been carried out and the results imply that this method is efficient and effective for small and medium-size enterprises to design KMS.
