An Improved Differential Evolution Algorithm Based on Adaptive Parameter

The differential evolution (DE) algorithm is a heuristic global optimization technique based on population which is easy to understand, simple to implement, reliable, and fast. The evolutionary parameters directly influence the performance of differential evolution algorithm. The adjustment of control parameters is a global behavior and has no general research theory to control the parameters in the evolution process at present. In this paper, we propose an adaptive parameter adjustment method which can dynamically adjust control parameters according to the evolution stage. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and faster convergence speed

An Improved Differential Evolution Algorithm Based on Statistical Log-linear Model

Differential evolution (DE) algorithm is a good optimization technique based on population which has been successfully applied in many research and application areas. Log-linear model is a statistical model which can easily blend multiple features, a variety of knowledge sources can be added to the model in the form of feature functions. Traditional differential evolution algorithm is easy to fall into local optimum value and the convergence rate is slow. To solve these problems, an improved differential evolution algorithm based on log-linear model is proposed and implemented in this paper. There are two mainly works in this paper. Firstly, we introduce log-linear model to differential evolution algorithm which can enhance decision making ability. Secondly, some operations are presented to improve global optimization capability. Experiments showed that the improved algorithm has more powerful global exploration ability and faster convergence speed

Log-Linear Model Based Behavior Selection Method for Artificial Fish Swarm Algorithm

Artificial fish swarm algorithm (AFSA) is a population based optimization technique inspired by social behavior of fishes. In past several years, AFSA has been successfully applied in many research and application areas. The behavior of fishes has a crucial impact on the performance of AFSA, such as global exploration ability and convergence speed. How to construct and select behaviors of fishes are an important task. To solve these problems, an improved artificial fish swarm algorithm based on log-linear model is proposed and implemented in this paper. There are three main works. Firstly, we proposed a new behavior selection algorithm based on log-linear model which can enhance decision making ability of behavior selection. Secondly, adaptive movement behavior based on adaptive weight is presented, which can dynamically adjust according to the diversity of fishes. Finally, some new behaviors are defined and introduced into artificial fish swarm algorithm at the first time to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and reasonable convergence speed compared with the standard artificial fish swarm algorithm

A Semi-Supervised User Group Identification Based on Synergetic Neural Network and Information Entropy

User group identification is an important task in intelligent personalized information service. A key problem of intelligence user server model is how to classify and indentify the user groups. Only when the user group can be effectively identified, the desired service can be offered. At present, it is difficult to obtain a large number of labeled corpuses which takes a certain amount of human and material resources. How to improve the comprehensive utilization of a small amount of labeled sample and a large number of unlabeled samples is an important task. To solve the problem, we propose novel semi-supervised user group identification based on SNN and information entropy in this paper. This paper has two main works. Firstly, a user group identification using synergetic neural network (SNN) is presented, which can effectively identify user groups; Secondly, we propose a noise filter based on information entropy to reduce the noise of expand data. The experiment results show the proposed model in this paper has a higher performance for user group identification, and provide a good practicability and a promising future for other tasks

Sharing Model of Online Overseas Chinese Education Resource Based on Cloud Computing

In recent years, with the sustainable and fast development of chinese economy, there has been increasing interest in overseas chinese language. As an important way of overseas chinese education, online education has been drawing more and more attention due to its simplicity and convenience. But at present, the existing chinese education resource service model is simply assigned the resources to users which can not effectively meet the practical needs of users. How to provide a personalized service is the key problem to be solved. In this paper, we proposed a sharing model of chinese education resource based on cloud computing. There are three mainly works in this paper. Firstly, user vector space is constructed based on user personal information. Secondly, synergetic neural network is presented to user group recognition. Finally, sharing model based cloud computing is presented and implemented. The proposed model in this paper provide a good practicability and a promising future for overseas chinese education

Cloud Synergetic Recommendation Model for Overseas Chinese Education by Modeling Multi-Source User Metaphor Information

With the deepening of global economic integration, overseas Chinese education has attracted more and more attention. How to quickly and accurately retrieve resources that meet user's personalized needs has become a key issue in the recommendation model of overseas Chinese education. To solve this problem, a cloud synergetic recommendation model for overseas Chinese education is proposed in this paper. Firstly, a user vector space with fine granularity representation is constructed by introducing multi-knowledge source emotional metaphor information. Secondly, considering the synergetic interaction among various user features, we proposed a synergetic user group recognition method based on synergetic theory. Finally, in order to improve the efficiency of resource acquisition, a recommendation model of Chinese education based on cloud computing is presented. The model proposed in this paper has certain reference significance for overseas Chinese education and online learning model

Personalized Overseas Chinese Education Model Based on Map-Reduce Model of Cloud Computing

The rapid updates of the resources and media in the big data age provide new opportunities for oversea Chinese education. It is an urgent task to effectively use the big data to boost the development of oversea Chinese education. However, very few studies are conducted in this area. Map-Reduce is a programming model of cloud computing used for the parallel computing of the large-scale data sets and this model enables programmers to run their own programs in the distributed system. In this paper we proposed a personalized overseas Chinese education model based on Map-Reduce mechanism , which can analyze the behavioral habits and personal preferences of users from a large pool of Chinese educational resources. In this way, the customer needs can be accurately grasped and their favorite resources are recommended from huge amounts of resources. The proposed model has a good application prospect for overseas Chinese education