Tag: Soft Computing – A Fusion of Foundations, Methodologies and Applications

Efficient approaches for summarizing subspace clusters into k representatives

Abstract  A major challenge in subspace clustering is that subspace clustering may generate an explosive number of clusters with high
computational complexity, which severely restricts the usage of subspace clustering. The problem gets even …

Abstract  

A major challenge in subspace clustering is that subspace clustering may generate an explosive number of clusters with high
computational complexity, which severely restricts the usage of subspace clustering. The problem gets even worse with the
increase of the data’s dimensionality. In this paper, we propose to summarize the set of subspace clusters into k representative clusters to alleviate the problem. Typically, subspace clusters can be clustered further into k groups, and the set of representative clusters can be selected from each group. In such a way, only the most representative
subspace clusters will be returned to user. Unfortunately, when the size of the set of representative clusters is specified,
the problem of finding the optimal set is NP-hard. To solve this problem efficiently, we present two approximate methods:
PCoC and HCoC. The greatest advantage of our methods is that we only need a subset of subspace clusters as the input instead
of the complete set of subspace clusters. Precisely, only the clusters in low-dimensional subspaces are computed and assembled
into representative clusters in high-dimensional subspaces. The approximate results can be found in polynomial time. Our performance
study shows both the effectiveness and efficiency of these methods.

  • Content Type Journal Article
  • Pages 1-9
  • DOI 10.1007/s00500-010-0552-8
  • Authors
    • Guanhua Chen, Peking University School of Electronics Engineering and Computer Science Beijing 100871 China
    • Xiuli Ma, Peking University School of Electronics Engineering and Computer Science Beijing 100871 China
    • Dongqing Yang, Peking University School of Electronics Engineering and Computer Science Beijing 100871 China
    • Shiwei Tang, Peking University School of Electronics Engineering and Computer Science Beijing 100871 China
    • Meng Shuai, Peking University School of Electronics Engineering and Computer Science Beijing 100871 China
    • Kunqing Xie, Peking University School of Electronics Engineering and Computer Science Beijing 100871 China

The smooth switching of double modes fuzzy control

Abstract  Various multimode controls are more and more widely applied in industry to improve the performance of control systems. Double
modes fuzzy control is one of multimode controls, which has two independent and different mode controller…

Abstract  

Various multimode controls are more and more widely applied in industry to improve the performance of control systems. Double
modes fuzzy control is one of multimode controls, which has two independent and different mode controllers to satisfy different
control demands. The smooth switching of different controllers is the key technology in industrial application of multimode
modes control. Double modes fuzzy control is used to improve the dynamic and steady-state performances of control systems.
This paper focuses on the unsteady problem at switching point of controllers in double modes control system. Three structures
of double modes fuzzy control systems are proposed and discussed. The design principles of multimode control are analyzed.
Three different switching methods are analyzed and their feasibility is studied. The concept of smooth switching from one
controller to another controller is proposed. Especially the smooth switching of fuzzy/PI double modes control is analyzed,
and the corresponding fuzzy controller is designed. The simulation of smooth switching at natural switching point of fuzzy/PI
double modes control system is carried out in order to prove the superiority of smooth switching at natural switching point.
The results of this paper can offer effective reference for other multimode control design.

  • Content Type Journal Article
  • Pages 1-8
  • DOI 10.1007/s00500-010-0550-x
  • Authors
    • Fang He, Harbin Institute of Technology School of Electrical Engineering and Automation Harbin China
    • Weiming Tong, Harbin Institute of Technology School of Electrical Engineering and Automation Harbin China
    • Shouhua Zhao, University of Jinan School of Control Science and Engineering Jinan China
    • Qiang Wang, University of Jinan School of Mechanical Engineering Jinan China

-DANTE: an ant colony oriented depth search procedure

Abstract  The

e
-Depth ANT Explorer (

e
–
DANTE
) algorithm applied to a multiple objective optimization problem is presented in this paper. This method is a hybridization
of the ant colony optimization algorithm with a depth search pro…

Abstract  

The

e

-Depth ANT Explorer (

e

–
DANTE
) algorithm applied to a multiple objective optimization problem is presented in this paper. This method is a hybridization
of the ant colony optimization algorithm with a depth search procedure, putting together an oriented/limited depth search.
A particular design of the pheromone set of rules is suggested for these kinds of optimization problems, which are an adaptation
of the single objective case. Six versions with incremental features are presented as an evolutive path, beginning in a single
colony approach, where no depth search is applied, to the final

e

–
DANTE
. Versions are compared among themselves in a set of instances of the multiple objective Traveling Salesman Problem. Finally,
our best version of

e

–
DANTE
 is compared with several established heuristics in the field showing some promising results.

  • Content Type Journal Article
  • Pages 1-34
  • DOI 10.1007/s00500-010-0543-9
  • Authors
    • Pedro Cardoso, University of Algarve, ISE Campus da Penha 8005-139 Faro Portugal
    • Mário Jesus, University of Algarve, ISE Campus da Penha 8005-139 Faro Portugal
    • Alberto Márquez, University of Sevilla ESTII Avda. Reina Mercedes 41012 Sevilla Spain

Extension of the ELECTRE method based on interval-valued fuzzy sets

Abstract  Decision-making is the process of finding the best option among the feasible alternatives. In classical multiple criteria
decision-making (MCDM) methods, the ratings and the weights of the criteria are known precisely. However, if …

Abstract  

Decision-making is the process of finding the best option among the feasible alternatives. In classical multiple criteria
decision-making (MCDM) methods, the ratings and the weights of the criteria are known precisely. However, if decision makers
cannot reach an agreement on the method of defining linguistic variables based on the fuzzy sets, the interval-valued fuzzy
set theory can provide a more accurate modeling. In this paper, the interval-valued fuzzy ELECTRE method is presented aiming
at solving MCDM problems in which the weights of criteria are unequal, using interval-valued fuzzy set concepts. For the purpose
of proving the validity of the proposed model, we present a numerical example and build a practical maintenance strategy selection
problem.

  • Content Type Journal Article
  • Pages 1-11
  • DOI 10.1007/s00500-010-0563-5
  • Authors
    • Behnam Vahdani, Islamic Azad University Qazvin Branch Department of Industrial and Mechanical Engineering Qazvin Iran
    • Hasan Hadipour, Islamic Azad University Qazvin Branch Department of Industrial and Mechanical Engineering Qazvin Iran

Solving multiple instances at once: the role of search and adaptation

Abstract  Having in mind the idea that the computational effort and knowledge gained while solving a problem’s instance should be used
to solve other ones, we present a new strategy that allows to take advantage of both aspects. The strate…

Abstract  

Having in mind the idea that the computational effort and knowledge gained while solving a problem’s instance should be used
to solve other ones, we present a new strategy that allows to take advantage of both aspects. The strategy is based on a set
of operators and a basic learning process that is fed up with the information obtained while solving several instances. The
output of the learning process is an adjustment of the operators. The instances can be managed sequentially or simultaneously
by the strategy, thus varying the information available for the learning process. The method has been tested on different
SAT instance classes and the results confirm that (a) the usefulness of the learning process and (b) that embedding problem
specific algorithms into our strategy, instances can be solved faster than applying these algorithms instance by instance.

  • Content Type Journal Article
  • Pages 1-18
  • DOI 10.1007/s00500-010-0564-4
  • Authors
    • Antonio D. Masegosa, University of Granada Models of Decision and Optimization Research Group, Department of Computer Science and AI 18071 Granada Spain
    • David A. Pelta, University of Granada Models of Decision and Optimization Research Group, Department of Computer Science and AI 18071 Granada Spain
    • Juan R. González, University of Granada Models of Decision and Optimization Research Group, Department of Computer Science and AI 18071 Granada Spain

Hybrid differential evolution and Nelder–Mead algorithm with re-optimization

Abstract  Nonlinear optimization algorithms could be divided into local exploitation methods such as Nelder–Mead (NM) algorithm and
global exploration ones, such as differential evolution (DE). The former searches fast yet could be easily …

Abstract  

Nonlinear optimization algorithms could be divided into local exploitation methods such as Nelder–Mead (NM) algorithm and
global exploration ones, such as differential evolution (DE). The former searches fast yet could be easily trapped by local
optimum, whereas the latter possesses better convergence quality. This paper proposes hybrid differential evolution and NM
algorithm with re-optimization, called as DE-NMR. At first a modified NM, called NMR is presented. It re-optimizes from the
optimum point at the first time and thus being able to jump out of local optimum, exhibits better properties than NM. Then,
NMR is combined with DE. To deal with equal constraints, adaptive penalty function method is adopted in DE-NMR, which relaxes
equal constraints into unequal constrained functions with an adaptive relaxation parameter that varies with iteration. Benchmark
optimization problems as well as engineering design problems are used to experiment the performance of DE-NMR, with the number
of function evaluation times being employed as the main index of measuring convergence speed, and objective function values
as the main index of optimum’s quality. Non-parametric tests are employed in comparing results with other global optimization
algorithms. Results illustrate the fast convergence speed of DE-NMR.

  • Content Type Journal Article
  • Pages 1-14
  • DOI 10.1007/s00500-010-0566-2
  • Authors
    • Zhenxiao Gao, Tsinghua University Department of Automation Beijing China
    • Tianyuan Xiao, Tsinghua University Department of Automation Beijing China
    • Wenhui Fan, Tsinghua University Department of Automation Beijing China

Generalized intuitionistic fuzzy geometric aggregation operator and its application to multi-criteria group decision making

Abstract  In general, for multi-criteria group decision making problem, there exist inter-dependent or interactive phenomena among criteria
or preference of experts, so that it is not suitable for us to aggregate them by conventional aggrega…

Abstract  

In general, for multi-criteria group decision making problem, there exist inter-dependent or interactive phenomena among criteria
or preference of experts, so that it is not suitable for us to aggregate them by conventional aggregation operators based
on additive measures. In this paper, based on fuzzy measures a generalized intuitionistic fuzzy geometric aggregation operator
is investigated for multiple criteria group decision making. First, some operational laws on intuitionistic fuzzy values are
introduced. Then, a generalized intuitionistic fuzzy ordered geometric averaging (GIFOGA) operator is proposed. Moreover,
some of its properties are given in detail. It is shown that GIFOGA operator can be represented by special t-norms and t-conorms
and is a generalization of intuitionistic fuzzy ordered weighted geometric averaging operator. Further, an approach to multiple
criteria group decision making with intuitionistic fuzzy information is developed where what criteria and preference of experts
often have inter-dependent or interactive phenomena among criteria or preference of experts is taken into account. Finally,
a practical example is provided to illustrate the developed approaches.

  • Content Type Journal Article
  • Pages 1-10
  • DOI 10.1007/s00500-010-0554-6
  • Authors
    • Chunqiao Tan, Central South University School of Business Changsha 410083 China

Image annotation by incorporating word correlations into multi-class SVM

Abstract  Image annotation systems aim at automatically annotating images with semantic keywords. Machine learning approaches are often
used to develop these systems. In this paper, we propose an image annotation approach by incorporating wo…

Abstract  

Image annotation systems aim at automatically annotating images with semantic keywords. Machine learning approaches are often
used to develop these systems. In this paper, we propose an image annotation approach by incorporating word correlations into
multi-class support vector machine (SVM). At first, each image is segmented into five fixed-size blocks instead of time-consuming
object segmentation. Every keyword from training images is manually assigned to the corresponding block and word correlations
are computed by a co-occurrence matrix. Then, MPEG-7 visual descriptors are applied to these blocks to represent visual features
and the minimal-redundancy-maximum-relevance (mRMR) method is used to reduce the feature dimension. A block-feature-based
multi-class SVM classifier is trained for 80 semantic concepts. At last, the probabilistic outputs from SVM and the word correlations
are integrated to obtain the final annotation keywords. The experiments on Corel 5000 dataset demonstrate our approach is
effective and efficient.

  • Content Type Journal Article
  • Pages 1-11
  • DOI 10.1007/s00500-010-0558-2
  • Authors
    • Lei Zhang, Shandong University School of Computer Science and Technology Jinan China
    • Jun Ma, Shandong University School of Computer Science and Technology Jinan China

Variable precision rough set model over two universes and its properties

Abstract  The extension of rough set model is an important research direction in the rough set theory. In this paper, based on the rough
set model over two universes, we firstly propose the variable precision rough set model (VPRS-model) ove…

Abstract  

The extension of rough set model is an important research direction in the rough set theory. In this paper, based on the rough
set model over two universes, we firstly propose the variable precision rough set model (VPRS-model) over two universes using
the inclsion degree. Meantime, the concepts of the reverse lower and upper approximation operators are presented. Afterwards,
the properties of the approximation operators are studied. Finally, the approximation operators with two parameters are introduced
as a generalization of the VPRS-model over two universes, and the related conclusions are discussed.

  • Content Type Journal Article
  • Pages 1-11
  • DOI 10.1007/s00500-010-0562-6
  • Authors
    • Yonghong Shen, Tianshui Normal University School of Mathematics and Statistics Tianshui 741001 People’s Republic of China
    • Faxing Wang, Tongda College of Nanjing University of Posts and Telecommunications Nanjing 210046 People’s Republic of China

A modified hybrid method of spatial credibilistic clustering and particle swarm optimization

Abstract  Hybrid methods of spatial credibilistic clustering and particle swarm optimization (SCCPSO) (Wen et al. in Int J Fuzzy Syst
10:174–184, 2008) are validated to be effective, and produce better results than other common method…

Abstract  

Hybrid methods of spatial credibilistic clustering and particle swarm optimization (SCCPSO) (Wen et al. in Int J Fuzzy Syst
10:174–184, 2008) are validated to be effective, and produce better results than other common methods. In this paper, SCCPSO is further investigated
and a modified SCCPSO is put forward by discussing the membership functions and presenting a pre-selection method based on
proving an evaluation criterion on the clustering results. The analysis of computational complexity demonstrates the feasibility
of the modified SCCPSO. Experiments verify the discussion on the membership functions, the correctness of the evaluation criterion,
as well as the effectiveness of the pre-selection method and the modified SCCPSO.

  • Content Type Journal Article
  • Pages 1-11
  • DOI 10.1007/s00500-010-0553-7
  • Authors
    • Peihan Wen, Tsinghua University Department of Industrial Engineering Beijing 100084 China
    • Jian Zhou, Tsinghua University Department of Industrial Engineering Beijing 100084 China
    • Li Zheng, Tsinghua University Department of Industrial Engineering Beijing 100084 China