Ruochen Liu

An improved cooperative quantum-behaved particle swarm optimization

Particle swarm optimization (PSO) is a population-based stochastic optimization. Its parameters are easy to control, and it operates easily. But, the particle swarm optimization is a local convergence algorithm. Quantum-behaved particle swarm optimization (QPSO) overcomes this shortcoming, and outperforms original PSO. Based on classical QPSO, cooperative quantum-behaved particle swarm optimization (CQPSO) is present. This CQPSO, a particle firstly obtaining several individuals using Monte Carlo method and these individuals cooperate between them. In the experiments, five benchmark functions and six composition functions are used to test the performance of CQPSO. The results show that CQPSO performs much better than the other improved QPSO in terms of the quality of solution and computational cost.

Gene transposon based clone selection algorithm for automatic clustering

Inspired by the principle of gene transposon proposed by Barbara McClintock, a new immune computing algorithm for automatic clustering named as Gene Transposon based Clone Selection Algorithm (GTCSA) is proposed in this paper. The proposed algorithm does not require a prior knowledge of the number of clusters; an improved variant of the clonal selection algorithm is used to determine the satisfied number of clusters and the appropriate partitioning of the data set as well. In addition, a novel operation called antibody gene transposon is introduced to the framework of clonal selection algorithm which can realize to find the satisfied number of cluster automatically. The proposed method has been extensively compared with iterated local search approach (ILS) and three well-known automatic clustering algorithms, including automatic clustering using an improved differential evolution algorithm (ACDE); variable-string-length genetic algorithm based clustering techniques (VGA) and the dynamic clustering approach based on particle swarm optimization (DCPSO). 23 datasets with widely varying characteristics are used to demonstrate the superiority of the GTCSA. In addition, GTCSA is applied to a real world application, namely natural image segmentation, with a good performance obtained.

A sphere-dominance based preference immune-inspired algorithm for dynamic multi-objective optimization

Real-world optimization involving multiple objectives in changing environment known as dynamic multi-objective optimization (DMO) is a challenging task, especially special regions are preferred by decision maker (DM). Based on a novel preference dominance concept called sphere-dominance and the theory of artificial immune system (AIS), a sphere-dominance preference immune-inspired algorithm (SPIA) is proposed for DMO in this paper. The main contributions of SPIA are its preference mechanism and its sampling study, which are based on the novel sphere-dominance and probability statistics, respectively. Besides, SPIA introduces two hypermutation strategies based on history information and Gaussian mutation, respectively. In each generation, which way to do hypermutation is automatically determined by a sampling study for accelerating the search process. Furthermore, The interactive scheme of SPIA enables DM to include his/her preference without modifying the main structure of the algorithm. The results show that SPIA can obtain a well distributed solution set efficiently converging into the DMs preferred region for DMO.

A particle swarm optimization based simultaneous learning framework for clustering and classification

A particle swarm optimization based simultaneous learning framework for clustering and classification (PSOSLCC) is proposed in this paper. Firstly, an improved particle swarm optimization (PSO) is used to partition the training samples, the number of clusters must be given in advance, an automatic clustering algorithm rather than the trial and error is adopted to find the proper number of clusters, and a set of clustering centers is obtained to form classification mechanism. Secondly, in order to exploit more useful local information and get a better optimizing result, a global factor is introduced to the update strategy update strategy of particle in PSO. PSOSLCC has been extensively compared with fuzzy relational classifier (FRC), vector quantization and learning vector quantization (VQ+LVQ3), and radial basis function neural network (RBFNN), a simultaneous learning framework for clustering and classification (SCC) over several real-life datasets, the experimental results indicate that the proposed algorithm not only greatly reduces the time complexity, but also obtains better classification accuracy for most datasets used in this paper. Moreover, PSOSLCC is applied to a real world application, namely texture image segmentation with a good performance obtained, which shows that the proposed algorithm has a potential of classifying the problems with large scale. A particle swarm optimization based simultaneous learning framework for clustering and classification (PSOSLCC) is proposed.The simultaneous frame consists of two parts: clustering section and classification section.An automatic clustering algorithm is used to find the proper number of clusters.An improved particle swarm optimization with a global factor is used in the training phase.The performance of PSOSLCC has been extensively compared with four state-of-the-art classification algorithms over a test suit of datasets and texture image segmentation.

Quantum evolutionary clustering algorithm based on watershed applied to SAR image segmentation

Abstract The goal of segmentation is to partition an image into disjoint regions. In this paper, the segmentation problem based on partition clustering is viewed as a combinatorial optimization problem. A new algorithm called a quantum evolutionary clustering algorithm based on watershed (QWC) is proposed. In the new algorithm, the original image is first partitioned into small pieces by watershed algorithm, and the quantum-inspired evolutionary algorithm is used to search the optimal clustering center, and finally obtain the segmentation result. Experimental results show that the proposed method is effective for texture image and SAR image segmentation, compared with QICW, the genetic clustering algorithm based on watershed (W-GAC) and K-means algorithm based on watershed (W-KM).

Modified Co-Training With Spectral and Spatial Views for Semisupervised Hyperspectral Image Classification

Hyperspectral images are characterized by limited labeled samples, large number of spectral channels, and existence of noise and redundancy. Supervised hyperspectral image classification is difficult due to the unbalance between the high dimensionality of the data and the limited labeled training samples available in real analysis scenarios. The collection of labeled samples is generally hard, expensive, and time-consuming, whereas unlabeled samples can be obtained much easier. This observation has fostered the idea of adopting semisupervised learning techniques in hyperspectral image classification. In this paper, a semisupervised method based on a modified co-training process with spectral and spatial views is proposed for hyperspectral image classification. The original spectral features and the 2-D Gabor features extracted from spatial domains are adopted as two distinct views for co-training, which considers both the spectral and spatial information. Then, a modified co-training process with a new sample selection scheme is presented, which can effectively improve the co-training performance, especially when there are extremely limited labeled samples available. Experiments carried out on two real hyperspectral images show the superiority of the proposed semisupervised method with the modified co-training process over the corresponding supervised techniques, the semisupervised method with the conventional co-training version, and the semisupervised graph-based method.

Immunodomaince based clonal selection clustering algorithm

Based on clonal selection principle and the immunodominance theory, a new immune clustering algorithm, Immunodomaince based Clonal Selection Clustering Algorithm (ICSCA) is proposed in this paper. An immunodomaince operator is introduced to the clonal selection algorithm, which can realize on-line gaining prior knowledge and sharing information among different antibodies. The proposed method has been extensively compared with Fuzzy C-means (FCM), Genetic Algorithm based FCM (GAFCM) and Clonal Selection Algorithm based FCM (CSAFCM) over a test suit of several real life datasets and synthetic datasets. The result of experiment indicates the superiority of the ICSCA over FCM, GAFCM and CSAFCM on stability and reliability for its ability to avoid trapping in local optimum.

Dynamic local search based immune automatic clustering algorithm and its applications

Ring topology of neighborhood in Mutation Strategy 2. Besides four different local search operation includes external cluster swapping, internal cluster swapping, cluster addition and cluster decrease is proposed to realize variation of the number of clusters during evolution, a neighborhood structure based hybrid mutation strategy provides a proper tradeoff between exploration and exploitation. Dynamic local search based immune automatic clustering algorithm (DLSIAC) is proposed to automatically evolve the number of clusters as well as a proper partitioning of data sets.A dynamic local search is designed to find the optimal number of clusters with a fast speed.The dynamic local search includes external cluster swapping, internal cluster swapping, cluster addition and cluster decrease.A new neighborhood structure based hybrid mutation strategy is proposed to further improve the performance of the algorithm.DLSIAC is also applied to several texture images and SAR images segmentation, with a good performance obtained. Based on clonal selection mechanism in immune system, a dynamic local search based immune automatic clustering algorithm (DLSIAC) is proposed to automatically evolve the number of clusters as well as a proper partition of datasets. The real based antibody encoding consists of the activation thresholds and the clustering centers. Then based on the special structures of chromosomes, a particular dynamic local search scheme is proposed to exploit the neighborhood of each antibody as much as possible so to realize automatic variation of the antibody length during evolution. The dynamic local search scheme includes four basic operations, namely, the external cluster swapping, the internal cluster swapping, the cluster addition and the cluster decrease. Moreover, a neighborhood structure based clonal mutation is adopted to further improve the performance of the algorithm. The proposed algorithm has been extensively compared with five state-of-the-art automatic clustering techniques over a suit of datasets. Experimental results indicate that the DLSIAC is superior to other five clustering algorithms on the optimum number of clusters found and the clustering accuracy. In addition, DLSIAC is applied to a real problem, namely image segmentation, with a good performance.

A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive model

Dynamic multi-objective optimization problem (DMOP) is quite challenging and it dues to that there are multiple conflicting objects changing over with time or environment. In this paper, a novel cooperative coevolutionary dynamic multi-objective optimization algorithm (PNSCCDMO) is proposed. The main idea of a new cooperative coevolution based on non-dominated sorting is that it allows the decomposition process of the optimization problem according to the search space of decision variables, and each species subcomponents will cooperate to evolve for better solutions. This way derives from nature and can improve convergence significantly. A modified linear regression prediction strategy is used to make rapid response to the new changes in the environment. The effectiveness of PNSCCDMO is validated against various of DMOPs compared with the other four algorithms, and the experimental result indicates PNSCCDMO has a good capability to track the Pareto front as it is changed with time in dynamic environments.

Clonal Strategy Algorithm Based on the Immune Memory

Based on the clonal selection theory and immune memory mechanism in the natural immune system, a novel artificial immune system algorithm, Clonal Strategy Algorithm based on the Immune Memory (CSAIM), is proposed in this paper. The algorithm realizes the evolution of antibody population and the evolution of memory unit at the same time, and by using clonal selection operator, the global optimal computation can be combined with the local searching. According to antibody-antibody (Ab-Ab) affinity and antibody-antigen (Ab-Ag) affinity, the algorithm can allot adaptively the scales of memory unit and antibody population. It is proved theoretically that CSAIM is convergent with probability 1. And with the computer simulations of eight benchmark functions and one instance of traveling salesman problem (TSP), it is shown that CSAIM has strong abilities in having high convergence speed, enhancing the diversity of the population and avoiding the premature convergence to some extent.