Text Information Is Based On Cultural Particle Swarm Optimization Filtration

For the past few years, the rapid development of the network technology, lead to adramatic increase of information content. Rich information resources to make it easier for usersto obtain the required knowledge, but also people are plagued by garbage information andredundant information. The rubbish information and redundant information not only affects theefficiency and the quality of network, but also affected the healthy development of the network.Therefore, how to obtaining the needed information from the network, and can effectivelyprevent the irrelevant information and illegal information, has become one of the major task ofthe current network research field.Information Filtering Technology is an effective method in positioning of interest to theuser information, shielding bad information according to customer requirements in the dynamicflow of information. Text information filtering includes many aspects, and network informationfiltering is one of the branches, due to network most of the information on the form of text, sothe research of this paper is mainly on text information filtering. In this paper, the textinformation filtering on the network involved in some of the key technologies are discussed.Then the culture particle swarm optimization to make improvements, and finally to improvecultural particle swarm optimization applied to the filter template. The main work of this paperincludes the following three aspects:1. Proposed a new adaptive dynamic cultural particle swarm optimization algorithmto solve the problem of particle swarm optimization algorithm easy to fall into localoptimum in solving complex problems.The evaluation of particle swarm premature convergence indicators was introduced intopopulation space. By calculating the evaluation of particle swarm premature convergenceindicators, decisions were made whether to have mutated operation on population space. Theimproved algorithm can make better use of mechanism of dual evolution and dual promotion incultural particle swarm optimization algorithm. The inertia weight of the particle was adjustedadaptively based on the premature convergence degree of the swarm. The diversity of inertiaweight makes a compromise between the global convergence and convergence speed. Theproposed algorithm was tested with four well-known benchmark functions. The experimentalresults show that the new algorithm-m has great global search ability convergence accuracy andconvergence velocity is also increased and avoid the premature convergence problemeffectively.2. The improved particle swarm algorithm applied to the cultural network templatetext information filtering optimization.The method with the improved adaptive dynamic cultural particle swarm algorithm applied to optimize the filter template, and according to the similarity, the classificationaccuracy, this paper proposes a particle fitness evaluation system. The experimental resultsshow that the use of the culture of the improved particle swarm algorithm to optimize networkinformation filtering of the user template, eventually improve the precision of text classification,achieve better filtering effect.3. Designed and implemented the text information filtering system based on adaptivedynamic cultural particle swarm algorithm.The improved particle swarm algorithm applied to the cultural network template textinformation filtering optimization. According to the participation of the user feedbackinformation to the user to dynamically update the template optimization and make the system tobetter meet the needs of users. According to the needs of users to realize real-time filtering ofnetwork information to improve the efficiency of filtering system, and ensure the filteringaccuracy and stability of the system.