Search

Genetic data is very important for biological research, but it is hard to be obtained by experiment. In this paper, we introduce an algorithm for generating simulated genetic data based on K-mediods. A concept of Cluster Channel is proposed in this algorithm and used to generate simulated data. The noise of origin data could be eliminated using the proposed method. The experimental results show reliability of simulated genetic data. SAM is used to analyze the simulated data and original data, and we get a conclusion that the simulated data can effectively validate differentially expressed gene detected algorithm.

In order to improve the clustering quality of the Affinity Propagation algorithm further and get more accurate number of clusters, this paper proposed a novel algorithm based on the Particles Swarm Optimization, which used In-Group Proportion index as fitness function to search the optimal preference of Affinity Propagation algorithm. Experimental results show that the predicted results had been tested with the novel proposed algorithm and the better results had been achieved.

A novel inferring network algorithm is proposed in this paper, which can be used to infer gene regulatory network. One of the contributions of this paper is a novel and effective optimal algorithm to search the best value of given function. Furthermore, the optimal algorithm can be used to find the best suitable gene regulatory network of given genes. Mostly, the singular value decomposition was used to reduce the scope of the solution of given problem. This method can avoid local convergence in the optimal algorithm mostly. The simulated data and real data which is Yeast Saccharomyces cerevisiae cell cycle gene expression profiles from the Spellman's data were used to test the efficiency of the proposed algorithm. And we compared our algorithm with traditional genetic algorithm and particle swarm optimization algorithm. the efficiency of the algorithm is demonstrated in this paper.

Face recognition plays a important role in computer vision. Recent researches show that high dimensional face images lie on or close to a low dimensional manifold. LPP is a widely used manifold reduced dimensionality technique. But it suffers two problem: (1) Small Sample Size problem; (2)the performance is sensitive to the neighborhood size k. In order to address the problems, this paper proposed a Matrix Exponential LPP. To void the singular matrix, the proposed algorithm introduced the matrix exponential to obtain more valuable information for LPP. The experiments were conducted on two face database, Yale and Georgia Tech. And the results proved the performances of the proposed algorithm was better than that of LPP.

Recovery of phenol from the wastewater produced in the multi-cogeneration system has been studied. Based on the experimental results and theoretical analysis, butyl acetate was selected as the extractant and 99.95% of the volatile phenols in the wastewater was recovered in five stage extraction. The solvent recovery in the distillation column and the stripping column were also considered. Moreover, Aspen Plus was used for the simulation of the phenols extraction in the extraction column. Final result shows that phenol is much more difficult to be recovered because of its lower distribution coefficiency among the volatile phenols. The least solvent flow can also be found at different theoretical stages for the special environmental restrictions.

We present a Condensation Nucleus Clustering (CNC) method based on our study of SVC algorithm. In CNC, data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the minimal enclosing sphere. We consider the inner data images as the rough estimate of each cluster's core, and they can be easily clustered. We name the groups of cluster result as Condensation Nucleus. Then assign the remaining data points into each cluster by linear discriminant analysis. Furthermore, we improve the CNC method to GCNC (Gradational Condensation Nucleus Clustering). In GCNC, the remaining data are assigned to each cluster gradationally. With the Condensation Nucleus bigger and the remaining data less, the Condensation Nucleus grow up to the final cluster results. We compare our methods with other similar clustering algorithm to demonstrate the performance of the proposed method on several datasets.

In this paper, we proposed a new data fusion method to infer gene regulatory networks based on differential equations model. After testing on several simulation and real data sets, and comparing with three other kinds of single fusion methods, the results show that our method is effective and better than other3 fusion methods in inferring networks.

Fuzzy C-Means is one of the clustering algorithms based on optimizing an objective function. The selection of the initial parameters of the number and the initial cluster centers play an important influence in the performance of the FCM. This paper proposes a new FCM clustering algorithm with two stages. The proposed algorithm not only resolves the problem of the initial choice of the cluster center effectively, but also decreases the time when clustering large volume of data. The computer simulation results show the effectivity and the superiority of the new algorithm.

Since the emergence of BLOG, it not only represents a new network technology, but also means the beginning of a new life style. How to utilize and mine the BLOG content which contains hidden sentiment and real-time update is a big challenge in the data-mining domain. As most of the existing method for network text's topic mining is achieved through clustering text's topic and label which are labeled by hand and often mismatched with the content of the text, the accuracy rating of this method is low. In this paper, we present a BLOG community mining method based on topic features. We define topic features as representation for the major contents of BLOG. Meanwhile, we present a BLOG hidden sentiment continuous evaluation algorithm, called CSETR (Continuous sentiment evaluation with time regulator). CSETR can be used to evaluate the continuous sentiment scores of topic features. Furthermore, it also consider changes in sentiment intensity over time. Lastly, through CSETR we will build up sentiment trend model for Chinese BLOG community.

Aiming at the inflexible re-weighting problem of relevance feedback (RF) in image retrieval, a re-weighting relevance feedback method utilizing particle swarm optimization (PSORW-RF) is proposed. Firstly, initialize feature weightings randomly, then use the variances of the positive and negative feedback samples' features as study principle, utilize particle swarm optimization (PSO) algorithm to optimize weightings according to user's retrieval requirement, and obtain retrieval results at last. Experiments show that the proposed algorithm is validity.

A new multi-objective evolutionary algorithm for Environmental/Economic power Dispatch (EED) problem based on Particle Swarm Optimization (PSO) is proposed in this paper. The new algorithm has adopted the maintenance method of Pareto candidate solution set based on the max-min distance density. The algorithm effectively guarantees the convergence of the algorithm and the diversity solutions. The performance of algorithm has been examined over the standard IEEE 30-bus six-generator test system, and other multi-objective evolutionary algorithm are compared. Testing and comparing results showed this paper algorithm is feasible and efficient.

This paper presents a new multiobjective evolutionary algorithm for Environmental/Economic power Dispatch (EED) problem based on Differential Evolution (DE). The proposed algorithm is different from the classical DE in the process of mutation. The mutation is carried out with three vectors; one is the local best, other is the global best and third one is selected as randomly. The improved mutation operation is more explicit directional than classic ED, and it push the trial vector quickly towards the global optima. It effectively guarantees the convergence of the algorithm and the diversity solutions. On this basis, a new multiobjective evolutionary algorithm is proposed to handle the EED. The performance of algorithm has been examined over the standard IEEE 30 bus six generator test system, and other multi-objective evolutionary algorithm are compared. Testing and comparing results showed the effectiveness of the algorithm.

A method of multi-view human action recognition based on multi-scale features via tensor analysis is proposed. A series of silhouettes are transformed to a Serials-Frame image, from which the multi-scale features are extracted to construct the eigenSpace of a tensor, which named Serials-Frame Tensor(SF-Tensor). The SF-Tensor subspace analysis is applied to separate the variable views and people information to recognize different actions. Experiment results obtained show that the proposed method attains a good recognition rate and improves the efficiency significantly.

The hand gesture recognition plays a key role in many appealing applications. The sign language recognition is one of the important applications of hand gesture recognition. The existing methods on sign language recognition are limited to certain view. In this paper, we use tensor subspace analysis to model a multi-view hand gesture to recognize 26 manual alphabetical letters. In our experiment, each hand gesture is captured from 5 different views. Two experiments are conducted on gray-scale images and binary images, respectively. The results show the proposed method has a good performance on multi-view.

Advantages and disadvantages of two common algorithms frequently used in the moving target detection: background subtraction method and frame difference method are analyzed and compared in this paper. Then based on the background subtraction method, a moving target detection algorithm is proposed. The background image used to process the next frame image is generated through superposition of the current frame image and the current background image with a certain probability. This algorithm makes the objects which stay long time to be a part of the background after a certain period of time, but not be detected as a part of foreground. The experimental results show that this algorithm can detect moving targets more effectively and precisely.

A novel chaotic immune algorithm (CImmune) is proposed to implement for parameter selection of Support Vector Regression (SVR). After adding chaotic local searching to the artificial immune procedure for parameter optimization of SVR, this method takes the advantages of both Chaos Optimization Algorithm (COA) and Artificial Immune Algorithm (AIA) to improve the effect of SVR efficiently. From experiments by cross validation on the simulated data and concrete compressive strength dataset from UCI, the results show that the proposed method has good capability of searching optimum and jumping out the local optimum easily in SVR model.

Particle swarm optimization(PSO) algorithm is applied in relevance feedback(RF) of image retrieval, which is combined with user feedback process effectively, The proposed method makes users' understanding to the retrieval goal become clear gradually, it could search image database effectively and comprehensively, and at the same time it could avoid the contradiction of efficiency and retrieval effect caused by multi-feedback. Experiments show that the proposed algorithm is validity.

The prediction of operons is critical to reconstruction of regulatory networks at the whole genome level. In this paper, a novel approach based on variable precision rough set model (VPRSM) is presented to prediction of operon. We use five effective features: max distance, min distance, gene strand direction information, scores of COG, and scores of gene order conservation. The proposed method is examined on Escherichia coli K12 and an accuracy of 89.4% is obtained. We also compare this method with C4.5 and BP. The results indicate that VPRSM based decision tree classifier is an effective classifier for predicting operon.

Differential Evolution (DE) is population based, direct search, global optimization new algorithm. DE is simple and efficient, so solving Multi-objective Optimization Problems (MOP) using DE has become a new hot research topic. It was found in the research process that there are deteriorations in the process of solving MOP based on DE. Due to deterioration occurs, thus the convergence can no longer be guaranteed for the algorithm, and the efficiency is reduced for solving. This paper proposed resolve methods correspond to these two deteriorations. Numerical experiments show the effectiveness of the proposed algorithm.

Immune algorithm is a rising algorithm which simulates the organism immune system by computer. One of the immune algorithms named clonal selection algorithm is widely used due to its adaptability, implicit parallelism and diversity. A fitness function is constructed in this paper by using protein folding restrictions, such as amino acids' hydrophobicity rule, protein’s secondary structure folding rule, degree and amount of contacts in contact map, and other special rules. The experiment results of the prediction of 200 non-homological proteins with different lengths show that, this improved clonal selection algorithm has good adaptability and high efficiency.

One-dimensional cutting-stock is one of the classic NP-hard problems in combinatorial optimization. It is widely applied in engineering technology and industrial production. In this paper, an improved ant colony optimization is proposed based on the optimized one-dimensional cutting-stock model. Aiming at the specific characteristics of the problem, a series of improvement strategies are proposed, including part encoding, solution path, state transition probability and pheromone updating rules. Then the concrete steps of algorithm are described. Through the analysis and comparison of experimental results, this method is proved high efficiency.

In this paper, a novel support vector clustering algorithm based on k-means (SVC-KM) is presented not only improve the SVC running speed, but also overcome the weaknesses of k-means algorithm. Firstly, SVC algorithm was employed to identify some samples as outliers and some others as intra-cluster points, so that it removed noise and extracted samples near to the cluster core. Then the proposed method used Minimum Spanning Tree Pruning (MSTP) strategy on those intra-cluster points to initialize the number of clusters and clustering centroids which would be the parameters for k-means algorithm. Finally, it run k-means algorithm on subset without outliers to obtain the clustering result and assigned outliers into the nearest cluster. Applying the proposed algorithm to several test datasets, the experiments results compared with initial SVC algorithm and classical k-means strongly validate the efficiency and feasibility of SVC-KM.

In this paper, we apply the least-square support vector machine (LS-SVM) to operon prediction of Escherichia coli (E.coli), with different combinations of intergenic distance, gene expression data, and phylogenetic profile. Experimental results demonstrate that the WO pairs tend to have shorter intergenic distances, higher correlation coefficient and much stronger relation of co-envoled between phylogenetic profiles. Also, we dealt with the data sets extracted from WOs? and TUBs?, processed the intergenic distances with log-energy entropy, de-noised the Pearson correlation coefficients of two genes expression data with wavelet transform, and computed the Hamming distances of two phylogenetic profiles. Then we trained LS-SVM using part of the data sets and tested the trained classifier model using the rest data sets. It shows that different combinations of features could affect the prediction results. When the combination of intergenic distance, gene expression data and phylogenetic profile is taken as the input of LS-SVM in the linear kernel type, good results can be obtained, of which the accuracy, sensitivity and specificity are 92.34%, 93.54%, and 90.73%, respectively.

The performance of support vector machines (SVM) drops significantly while facing imbalanced datasets, though it has been extensively studied and has shown remarkable success in many applications. Some researchers have pointed out that it is difficult to avoid such decrease when trying to improve the efficient of SVM on imbalanced datasets by modifying the algorithm itself only. Therefore, as the pretreatment of data, sampling is a popular strategy to handle the class imbalance problem since it re-balances the dataset directly. In this paper, we proposed a novel sampling method based on genetic algorithms (GA) to rebalance the imbalanced training dataset for SVM. In order to evaluating the final classifiers more impartiality, AUC (area under ROC curve) is employed as the fitness function in GA. The experimental results show that the sampling strategy based on GA outperforms the random sampling method. And our method is prior to individual SVM for imbalanced protein domain boundary prediction. The accuracy of the prediction is about 70% with the AUC value 0.905.

The L-K means hierarchy clustering is proposed to decrease the long time of searching in a huge scale face database by reducing retrieving range. The database is divided into several sub sets after clustering. One-step split is used to divide the massive sub set into a few of small sets. Data redundant technology and predefined thresh are worked on a class's edge elements which will be re-signed into some categories for correctly searching in sets. The experiment results show that the proposed methods can effectively improve the searching speed while ensuring similar retrieval accuracy to search in the whole database.

Various researches have shown that machine learning approaches can be successfully used to detect and classify cancer tissue samples by their gene expression patterns. In this paper, an entropy-based improved k-TSP method (Ik-TSP) is proposed. We calculate the entropy for each gene based on the gene expression profile, and then find the best threshold of entropy depending on LOOCV accuracy for each gene expression dataset. Finally we select key genes for each gene expression dataset according to the best threshold and use them to implement Ik-TSP method to classify the cancer. Compared to 7 cancer classifiers mentioned in this paper in 9 binary public gene expression datasets of human cancers, the Ik-TSP method achieves an average LOOCV accuracy of 95.39%, and improves 3% better than the k-TSP method. Simulated experimental results show that the proposed Ik-TSP method is applicable to classify human cancers.

Making full use of image information with its own to extract features is the crucial problem in content based image retrieval (CBIR). In this paper, quotient space(QS) granularity computing theory is imported into image retrieval field, granularity thinking in image retrieval is clarified, and a novel image retrieval method is proposed. Firstly, aiming at the different behaviors under different granularities, color and texture features are obtained respectively under different granularities, different quotient spaces are achieve; secondly, do the attribute combination to the obtained quotient spaces according to the quotient space granularity combination principle; and then realize image retrieval using the combined attribute function. Comparing with methods adopting single attribute feature the image retrieval method based on quotient space granularity combination utilizes the image information with its own in a more effective way. The experimental results demonstrate the feasibility and validity of the proposed method.

In this paper, an incremental learning based color iris recognition method is proposed. The inner and outer boundaries of the iris are localized employing Canny edge detection method and Hough transform algorithm in the red channel and the blue channel respectively. This method exploits some fusion strategies to combine the matching scores between the test image and the templates of each class. The strategies used in this paper are min, max, sum, product and weighted sum. The experiments were carried out on UBIRIS v1.0. The experiments show that, in some scenarios that acquisition conditions change leading to possible decrease of performance, because of its characteristic of learning in practice, the incremental learning method can handle the problem effectively.

Making full use of image information with its own to extract features is the crucial problem in content based image retrieval (CBIR). In this paper, we import quotient space granularity computing theory into image retrieval field, clarify the granularity thinking in image retrieval, and a novel image retrieval method is proposed. Firstly, aiming at the different behaviors under different granularities, obtain color features under different granularities, achieve different quotient spaces; secondly, do the attribute combination to the obtained quotient spaces according to the quotient space granularity combination principle; and then realize image retrieval using the combined attribute function. Comparing with methods adopting single attribute feature the image retrieval method based on quotient space granularity combination utilizes the image information with its own in a more effective way. The experimental results demonstrate the feasibility and validity of the proposed method.

In this paper, the global structure constraint model (GSC) is used to present the target objects, and the evolutionary algorithm, particle swarm optimization (PSO) is exploited for searching the optimal status of the target in the new images. The experimental results show that this method can achieve effective results.

In this paper, a kind of object representation model global structure constraint is presented, in which objects are described as constellations of points satisfied with their intrinsic specific global structure constraints. The spatial relations among all the patches of small color variations are extracted as shape model and the representative color information of patches are encoded and clustered as color model with color cluster information. Then, in the searching phase, mutual information is used as measurement with optimal algorithm to locate target objects in images by finding out the exactly matched position. In the experiment, we tested the approach on a collection of human face images and the results demonstrated the approach is simple, effective and efficient.

This paper presents a novel efficient method to find submatrices in Loss of Heterozygosity (LOH) data that represent the relationship between specific SNPs loci and particular types of lung cancer. The approach is shown to perform well by experimental results with the SNPs data of 113 samples consisting of 12 normal persons and 101 lung cancer patients. The analyses on results testified the effectiveness and accuracy of the method in discovering chromosome segments related to suppressor genes of lung cancer.

This paper defines a new correlation coefficient SMBS which using both quantity and tendency in gene expression changing to measure the similarity among genes. The SMBS applies to K-means clustering algorithm and the result shows more co-regulated genes are found comparing with the CLUSTER3.0.

In this paper, we first studied some fusion strategies for color images based iris recognition. The strategies include Min, Max, Sum, Product and Weighted Sum. The fusion takes place at the matching scores level of red, green and blue channels. The experiments were carried out on UBIRIS v1.0. No fusion strategy can guarantee an improvement compared to the red channel, which yields the best single channel performance. We present a semi-supervised learning algorithm for iris recognition. Two methods are employed to estimate the distance between a test image and a subject. Every test image is appended to a class as a new template according to the classification result. The experiments show that, in some scenarios leading to acquisition condition change, the semi-supervised learning method can improve the performance.

We present a novel object localization approach based on the global structure constraint model (GSC) and optimal algorithm. In GSC, Objects are described as constellations of points satisfied with their specific global structure constraints. The spatial relations among all the patches having stable color information and their representative color information around patches are encoded. Then, the searching algorithm, i.e. genetic algorithm, is used to locate target objects in images by finding out the exactly matched position. In the experiment, we tested the approach on a collection of human face images and the results demonstrated the approach is simple, effective and efficient.

This paper extracts automotive marketing information, constructs data warehouse, adopts an improved ID3 decision tree model and an association rule model to do data mining, and then obtains prediction information of automotive customers' behavior. Experimental and comparative results verify the validity and accuracy of the prediction results.

Aiming at the color distributing feature of image, a feature extraction and image retrieval algorithm using Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) is proposed, which is based on child-block partition and area division. Firstly, divide the image into child-blocks, DWT the color value of each block, and extract the low frequency coefficients as the color feature of this block, and then divide the image into areas, some matrixes are composed with the DWT coefficients in this area, which are SVDed to gain the retrieval feature vector in order to compute the difference between images. Experiments show that the proposed algorithm reduces dimension well, obtains good recall and precision, and has good retrieval effect.

In this paper, we present a method which only employs protein primary structure to predict protein-protein interactions. The statistical method is used to generate sequence features, which are normalized for satisfying experiments. Six parameters of physicochemical properties are calculated for each protein, including assessable residues, buried residues, hydrophobility, molecular weight, polarity and average area buried. The sequence features are extracted both from interaction proteins and non-interaction proteins. And BP neural network is used to classify two kinds of protein. The statistical evaluation of the BP neural network classifier shows that it performs well above 87% accuracy rate through 10-fold cross-validation. 2000 sequences which come from Scerevisiae yeast dataset are classified in our experimentation. The results demonstrate that 1780 sequences are classified correctly, which show that our proposed method is effective and feasible.

This paper proposed a multi-objective differential evolution algorithm based on max-min distance density. The algorithm proposed the definiteness of max-min distance density and a Pareto candidate solution set maintenance method, and ensured the diversity of the Pareto solution set. Using Pareto dominance relationship among individuals and max-min distance density ensured the convergence of the algorithm, realized solving multi-objective optimization problems. The proposed algorithm is applied to five ZDT test functions and compared with others multi-objective evolutionary algorithms. Experimental result and analysis show that the algorithm is feasible and efficient

Sequential pattern mining is one of the most important fields in data mining. In this paper, we propose a novel algorithm FSPAN (Fast Sequential Pattern mining algorithm) to do the sequence mining. FSPAN can mine all the frequent sequential patterns in large datasets and it integrates a depth-first traversal approach with an effective pruning mechanism. This pruning mechanism solves the problem of searching frequent sequences in a sequence database by searching frequent items or frequent itemsets, which makes this method very efficient. Moreover, the databases scanned via FSPAN keep shrinking quickly, which makes the algorithm more efficient when the sequential patterns are longer. Experiments on standard test data show that FSPAN is very effective.

Identification of transcription factor binding sites from the upstream regions of genes is a highly important and unsolved problem. In this paper, we propose a novel framework for using evolutionary algorithm to solve this challenging issue. Under this framework, we use two prevalent evolutionary algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to find unknown sites in a collection of relatively long intergenic sequences that are suspected of being bound by the same factor. This paper represents binding sites motif to position weight matrix (PWM) and introduces how to code PWM to genome for GA and how to code it to particle for PSO. We apply these two algorithms to 5 different yeast Saccharomyces Cerevisiae transcription factor binding sites and CRP binding sites. The results on Saccharomyces Cerevisiae show that it can find the correct binding sites motifs, and the result on CRP shows that these two algorithms can achieve more accuracy than MEME and Gibbs Sampler.

A novel fuzzy kernel clustering algorithm is presented based on Particle Swarm Optimization algorithm (PSO). The idea of the algorithm is firstly map the data in the original space to a high-dimensional feature space by using Mercer kernel functions where the data are expected to be more separable then perform Fuzzy C-means (FCM) in the high dimensional space. The iteration process is replaced by the PSO based on gradient descent of FCM in feature space, which makes the algorithm have a strong global searching capacity and avoids the local minimum problems of FCM. At the same time, FCM is no longer a large degree dependent on the initialization values. The results of simulation experiments show the feasibility and effectiveness of the kernel clustering algorithm based on PSO.

Characteristics of data stream make it difficult for the clustering algorithms to satisfy the requirements on efficiency and effectiveness. This paper proposes a data stream clustering algorithm on dual-tier structure which employs the agent method. In the on-line process, a set of agents working simultaneously collect similar data points into sub-clusters by applying a heuristic strategy. And in the off-line process, summary information from the on-line component will be further analyzed to obtain the final clusters. The algorithm also supports the time-window queries on streams. The empirical evidence shows that this method can obtain high-quality clusters with low time complexity. analysis over an arbitrary period of the stream etc. As for stream clustering, a common method is dividing the streaming data into chunks, and algorithms for static sets can be used on each sub-set separately (2). In recent years, stream algorithms have developed into a two-phase structure (3), (4). Usually, a dual framework includes two parts: the on-line component and the off-line component. The former is responsible for the fast but rough processing of streaming data and saving the summary information to meet the one-pass restriction while the latter takes advantage of the information to conduct high-level analysis. At present, stream algorithms are still facing some problems, for example: sensitive to the initial data points; bad quality of clusters due to the loss of global information caused by dividing the stream; high time complexity etc. A novel dual-tier clustering algorithm for data streams, AGCluStream, is proposed in this paper. The on-line algorithm uses agents to make similar points denser in local areas, and record the temporary distribution of data according to the pyramidal time frame (3). The off-line algorithm uses these records to conduct time-window analysis and higher-level clustering analysis. AGCluStream dose not divide the stream, and it adopts an incomplete-partition strategy to maintain the global information more effectively.

Identification of Transcription Factor Binding Sites (TFBS) from the upstream region of genes remains a highly important and unsolved problem particularly in higher eukaryotic genomes. In this paper, we propose a new approach to predict TFBS. This approach uses Position Weight Matrix (PWM) to represent Binding Sites and uses genetic algorithm (GA) to search the best matrix. A new coding method so called multiple-variable coding is proposed in GA. We apply it on two transcription factors reb1 and mg1. The result shows that this approach can find most of the known sites, which indicates that this method is very effective.

This paper constructs the appraisal index system of the industry competitiveness, compares the secondary industry competitiveness of the eight cities along Yangtse Rive in Jiangsu by the model of the grey incidence degree, and puts forward a series of countermeasures and suggestions to improve the secondary industry competitiveness of the Mid Jiangsu

The extraction of fuzzy rules is always a difficult problem to fuzzy system. We have proposed a pruning algorithm to optimize fuzzy neural network based on particle swarm optimization algorithm. It can evolve both the fuzzy neural network's topology and weighting parameters. In a real problem, it can automatically obtain the near-optimal structure of fuzzy neural network according to the requirements. The experiment has proved that the method is applicable and efficient.

The similarity of molecular mechanisms of the organisms that have been studied strongly suggests that all organisms of Earth had a common ancestor. Thus any set of species is related, and the relationship is called a phylogeny. Usually a phylogenetic tree can represent the relationship. The task of phylogenetics is to infer this tree from observations upon the existing organisms. At present, the accuracy of the tree is not still ideal enough to reconstruction through the computer simulation. This paper introduce particle swarm optimization algorithm, and the discrete particle swarm optimization algorithm is used to inference and optimize the phylogenetic tree for the first time, and show the actual effect of this algorithm through the experiment.

A modified particle swarm optimization was proposed to solve traveling salesman problem (TSP). The algorithm searched in the Cartesian continuous space, and constructed a mapping from continuous space to discrete permutation space of TSP, thus to implement the space transformation. Moreover, local search technique was introduced to enhance the ability to search, and chaotic operations were employed to prevent the particles from falling into local optima prematurely. Finally four benchmark problems in TSPLIB were tested to evaluate the performance of the algorithm. Experimental results indicate that the algorithm can find high quality solutions in a comparatively short time.

A fuzzy classification system model based on support vector machine is proposed in this paper. Reduction methods are developed to minimize the complexity of the system by reducing the linguistic terms in the fuzzy rules based on the similarity of fuzzy sets, and removing the redundant and inconsistent fuzzy rules. Finally, the particle swarm optimization is used to adjust the system parameters for compensating the deviation caused by the reduction. Experimental results show that the methods are feasible and effective.