Enhanced minimum attribute reduction based on quantum-inspired shuffled frog leaping algorithm

Attribute reduction in the rough set theory is an important feature selection method, but finding a minimum attribute reduction has been proven to be a non-deterministic polynomial （NP）-hard problem. Therefore, it is necessary to investigate some fast and effective approximate algorithms. A novel and enhanced quantum-inspired shuffled frog leaping based minimum attribute reduction algorithm （QSFLAR） is proposed. Evolutionary frogs are represented by multi-state quantum bits, and both quantum rotation gate and quantum mutation operators are used to exploit the mechanisms of frog population diversity and convergence to the global optimum. The decomposed attribute subsets are co-evolved by the elitist frogs with a quantum-inspired shuffled frog leaping algorithm. The experimental results validate the better feasibility and effectiveness of QSFLAR, comparing with some representa- tive algorithms. Therefore, QSFLAR can be considered as a more competitive algorithm on the efficiency and accuracy for minimum attribute reduction.

Dynamic self-adaptive ANP algorithm and its application to electric field simulation of aluminum reduction cell

Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.

A projection-domain iterative algorithm for metal artifact reduction by minimizing the total-variation norm and the negative-pixel energy

Metal objects in X-ray computed tomography can cause severe artifacts.The state-of-the-art metal artifact reduction methods are in the sinogram inpainting category and are iterative methods.This paper proposes a projectiondomain algorithm to reduce the metal artifacts.In this algorithm,the unknowns are the metal-affected projections,while the objective function is set up in the image domain.The data fidelity term is not utilized in the objective function.The objective function of the proposed algorithm consists of two terms:the total variation of the metalremoved image and the energy of the negative-valued pixels in the image.After the metal-affected projections are modified,the final image is reconstructed via the filtered backprojection algorithm.The feasibility of the proposed algorithm has been verified by real experimental data.

Fuzzy-GA based algorithm for optimal placement and sizing of distribution static compensator (DSTATCOM) for loss reduction of distribution network considering reconfiguration

This work presents a fuzzy based methodology for distribution system feeder reconfiguration considering DSTATCOM with an objective of minimizing real power loss and operating cost. Installation costs of DSTATCOM devices and the cost of system operation, namely, energy loss cost due to both reconfiguration and DSTATCOM placement, are combined to form the objective function to be minimized. The distribution system tie switches, DSTATCOM location and size have been optimally determined to obtain an appropriate operational condition. In the proposed approach, the fuzzy membership function of loss sensitivity is used for the selection of weak nodes in the power system for the placement of DSTATCOM and the optimal parameter settings of the DFACTS device along with optimal selection of tie switches in reconfiguration process are governed by genetic algorithm(GA). Simulation results on IEEE 33-bus and IEEE 69-bus test systems concluded that the combinatorial method using DSTATCOM and reconfiguration is preferable to reduce power losses to 34.44% for 33-bus system and to 45.43% for 69-bus system.

Integrating Variable Reduction Strategy With Evolutionary Algorithms for Solving Nonlinear Equations Systems

Nonlinear equations systems(NESs)are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots.Evolutionary algorithms(EAs)are one of the methods for solving NESs,given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run.Currently,the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs.By contrast,problem domain knowledge of NESs is investigated in this study,where we propose the incorporation of a variable reduction strategy(VRS)into EAs to solve NESs.The VRS makes full use of the systems of expressing a NES and uses some variables(i.e.,core variable)to represent other variables(i.e.,reduced variables)through variable relationships that exist in the equation systems.It enables the reduction of partial variables and equations and shrinks the decision space,thereby reducing the complexity of the problem and improving the search efficiency of the EAs.To test the effectiveness of VRS in dealing with NESs,this paper mainly integrates the VRS into two existing state-of-the-art EA methods(i.e.,MONES and DR-JADE)according to the integration framework of the VRS and EA,respectively.Experimental results show that,with the assistance of the VRS,the EA methods can produce better results than the original methods and other compared methods.Furthermore,extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.

Attribute Reduction of Neighborhood Rough Set Based on Discernment

For neighborhood rough set attribute reduction algorithms based on dependency degree,a neighborhood computation method incorporating attribute weight values and a neighborhood rough set attribute reduction algorithm using discernment as the heuristic information was proposed.The reduction algorithm comprehensively considers the dependency degree and neighborhood granulation degree of attributes,allowing for a more accurate measurement of the importance degrees of attributes.Example analyses and experimental results demonstrate the feasibility and effectiveness of the algorithm.

Stable Model Order Reduction Method for Fractional-Order Systems Based on Unsymmetric Lanczos Algorithm

This study explores a stable model order reduction method for fractional-order systems. Using the unsymmetric Lanczos algorithm, the reduced order system with a certain number of matched moments is generated. To obtain a stable reduced order system, the stable model order reduction procedure is discussed. By the revised operation on the tridiagonal matrix produced by the unsymmetric Lanczos algorithm, we propose a reduced order modeling method for a fractional-order system to achieve a satisfactory fitting effect with the original system by the matched moments in the frequency domain. Besides, the bound function of the order reduction error is offered. Two numerical examples are presented to illustrate the effectiveness of the proposed method.

Model reduction using the genetic algorithmand routh approxi mations

A new method of model reduction combining the genetic algorithm（GA） with the Routh approximation method is presented. It is suggested that a high-order system can be approximated by a low-order model with a time delay. The denominator parameters of the reduced-order model are determined by the Routh approximation method, then the numerator parameters and time delay are identified by the GAL. The reduced-order models obtained by the proposed method will always be stable if the original system is stable and produce a good approximation to the original system in both the frequency domain and time domain. Two numerical examples show that the method is cornputationally simple and efficient.

A POTENTIAL REDUCTION ALGORITHM FOR LINEARLY CONSTRAINED CONVEX PROGRAMMING

A potential reduction algorithm is proposed for optimization of a convex function subject to linear constraints.At each step of the algorithm,a system of linear equations is solved to get a search direction and the Armijo's rule is used to determine a stepsize.It is proved that the algorithm is globally convergent.Computational results are reported.

ARNOLDI REDUCTION ALGORITHM FOR LARGE SCALE GYROSCOPIC EIGENVALUE PROBLEM

Based on Arnoldi's method, a version of generalized Arnoldi algorithm has been developed for the reduction of gyroscopic eigenvalue problems. By utilizing the skew symmetry of system matrix, a very simple recurrence scheme, named gyroscopic Arnoldi reduction algorithm has been obtained, which is even simpler than the Lanczos algorithm for symmetric eigenvalue problems. The complex number computation is completely avoided. A restart technique is used to enable the reduction algorithm to have iterative characteristics. It has been found that the restart technique is not only effective for the convergence of multiple eigenvalues but it also furnishes the reduction algorithm with a technique to check and compute missed eigenvalues. By combining it with the restart technique, the algorithm is made practical for large-scale gyroscopic eigenvalue problems. Numerical examples are given to demonstrate the effectiveness of the method proposed.

Heuristic Reduction Algorithm Based on Pairwise Positive Region

To guarantee the optimal reduct set, a heuristic reduction algorithm is proposed, which considers the distinguishing information between the members of each pair decision classes. Firstly the pairwise positive region is defined, based on which the pairwise significance measure is calculated between the members of each pair classes. Finally the weighted pairwise significance of attribute is used as the attribute reduction criterion, which indicates the necessity of attributes very well. By introducing the noise tolerance factor, the new algorithm can tolerate noise to some extent. Experimental results show the advantages of our novel heuristic reduction algorithm over the traditional attribute dependency based algorithm.

Attribute reduction algorithm based on discernibility for decision table

The attribute reduction algorithms of decision table based on discernible matrix are required to construct discernible matrix, which reduces efficiency of algorithms. In this paper, the relationship between attribute discernible matrix and its discernibility is first established for general information systems. Based on the idea that the equivalent discernible matrix has a same attribute reduction, existing matrices are modified and a formula of attribute discernibility associated with algebraic reduction for decision table is proposed. A heuristic attribute reduction algorithm based on attribute discernibility is presented. Experimental results indicate that the algorithm can more easily explore an optimal or sub-optimal reduction, and is efficient.

Fault Attribute Reduction of Oil Immersed Transformer Based on Improved Imperialist Competitive Algorithm

The original fault data of oil immersed transformer often contains a large number of unnecessary attributes,which greatly increases the elapsed time of the algorithm and reduces the classification accuracy,leading to the rise of the diagnosis error rate.Therefore,in order to obtain high quality oil immersed transformer fault attribute data sets,an improved imperialist competitive algorithm was proposed to optimize the rough set to discretize the original fault data set and the attribute reduction.The feasibility of the proposed algorithm was verified by experiments and compared with other intelligent algorithms.Results show that the algorithm was stable at the 27th iteration with a reduction rate of 56.25%and a reduction accuracy of 98%.By using BP neural network to classify the reduction results,the accuracy was 86.25%,and the overall effect was better than those of the original data and other algorithms.Hence,the proposed method is effective for fault attribute reduction of oil immersed transformer.

Reduction in Complexity of the Algorithm by Increasing the Used Memory - An Example

An algorithm complexity, or its efficiency, meaning its time of evaluation is the focus of primary care in algorithmic problems solving. Raising the used memory may reduce the complexity of algorithm drastically. We present an example of two algorithms on finite set, where change the approach to the same problem and introduction a memory array allows decrease the complexity of the algorithm from the order O(n2) up to the order O(n).

Soft-output stack algorithm with lattice-reduction for MIMO detection

A computationally efficient soft-output detector with lattice-reduction （LR） for the multiple-input multiple-output （MIMO） systems is proposed. In the proposed scheme, the sorted QR de- composition is applied on the lattice-reduced equivalent channel to obtain the tree structure. With the aid of the boundary control, the stack algorithm searches a small part of the whole search tree to generate a handful of candidate lists in the reduced lattice. The proposed soft-output algorithm achieves near-optimal perfor- mance in a coded MIMO system and the associated computational complexity is substantially lower than that of previously proposed methods.

A Hybrid Genetic Algorithm for Reduct of Attributes in Decision System Based on Rough Set Theory

Knowledge reduction is an important issue when dealing with huge amounts of data. And it has been proved that computing the minimal reduct of decision system is NP-complete. By introducing heuristic information into genetic algorithm, we proposed a heuristic genetic algorithm. In the genetic algorithm, we constructed a new operator to maintaining the classification ability. The experiment shows that our algorithm is efficient and effective for minimal reduct, even for the special example that the simple heuristic algorithm can’t get the right result.

Metropolitan Pollution Reduction by Intelligent Negotiation

This paper discusses the issue of pollution reduction in metropolises by means of intelligent negotiation in multi-agent systems. For situations of complete information, it gives a stochastic hill-climbing search algorithm for computing the pollution-reduction solutions; For situations of incomplete information, it puts forward a genetic algorithm for computing the best solutions for every plants subjectively and proposes market-mechanism-based algorithm for computing the emission-redistribution solutions objectively. Key words intelligent negotiation - game theory - pollution reduction - genetic algorithm CLC number TP 391.1 Foundation item: Supported by the National 863 Project (2002AA134020-04)Biography: HAN Wei (1975-) male, Ph.D. candidate, research direction: MAS and Electronic Commercial.

Maintenance decision-making method for manufacturing system based on cost and arithmetic reduction of intensity model

A cost-based selective maintenance decision-making method was presented.The purpose of this method was to find an optimal choice of maintenance actions to be performed on a selected group of machines for manufacturing systems.The arithmetic reduction of intensity model was introduced to describe the influence on machine failure intensity by different maintenance actions (preventive maintenance,minimal repair and overhaul).In the meantime,a resolution algorithm combining the greedy heuristic rules with genetic algorithm was provided.Finally,a case study of the maintenance decision-making problem of automobile workshop was given.Furthermore,the case study demonstrates the practicability of this method.

Unsupervised Quick Reduct Algorithm Using Rough Set Theory

Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. The conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. In this paper, we propose a new unsupervised quick reduct （QR） algorithm using rough set theory. The quality of the reduced data is measured by the classification performance and it is evaluated using WEKA classifier tool. The method is compared with existing supervised methods and the result demonstrates the efficiency of the proposed algorithm.

A Distributed Computing Framework Based on Lightweight Variance Reduction Method to Accelerate Machine Learning Training on Blockchain

To security support large-scale intelligent applications,distributed machine learning based on blockchain is an intuitive solution scheme.However,the distributed machine learning is difficult to train due to that the corresponding optimization solver algorithms converge slowly,which highly demand on computing and memory resources.To overcome the challenges,we propose a distributed computing framework for L-BFGS optimization algorithm based on variance reduction method,which is a lightweight,few additional cost and parallelized scheme for the model training process.To validate the claims,we have conducted several experiments on multiple classical datasets.Results show that our proposed computing framework can steadily accelerate the training process of solver in either local mode or distributed mode.