This paper considers two additional factors of the widely researched vehicle routing problem with time windows (VRPTW). The two factors, which are very common characteristics in realworld, are uncertain number of vehi...
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This paper considers two additional factors of the widely researched vehicle routing problem with time windows (VRPTW). The two factors, which are very common characteristics in realworld, are uncertain number of vehicles and simultaneous delivery and pick-up service. Using minimization of the total transport costs as the objective of the extension VRPTW, a mathematic model is constructed. To solve the problem, an efficient multiswarm cooperative particle swarm optimization (MCPSO) algorithm is applied. And a new encoding method is proposed for the extension VRPTW. Finally, comparing with genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, the MCPSO algorithm performs best for solving this problem.
In this paper we develop efficient numerical schemes to solve ordinary differential equations. Our methods are of the Nordsieck type, adaptive and capable of automatically controlling the global error of a numerical s...
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In this paper we develop efficient numerical schemes to solve ordinary differential equations. Our methods are of the Nordsieck type, adaptive and capable of automatically controlling the global error of a numerical solution. A special feature of the new stepsize selection algorithms introduced here is the global error estimation quality control. Two different ways of attaining the preassigned accuracy of computation are examined in the paper. Namely, we implement the global error control mechanism based on reducing the maximum stepsize bound and the other one is based on reducing the local error tolerance. An accurate starting procedure for the adaptive Nordsieck methods is presented in full detail. Our intention here is to find the most effective strategy of stepsize selection. Theoretical investigation is supplied with numerical tests.
In path planning problems, the most important task is to find a suitable collision-free path which satisfies some certain criteria ( the shortest path length, security, feasibility, smoothness, and so on), so defining...
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In path planning problems, the most important task is to find a suitable collision-free path which satisfies some certain criteria ( the shortest path length, security, feasibility, smoothness, and so on), so defining a suitable curve to describe path is essential. Three different commonly used curves are compared and discussed based on their performance on solving a set of path planning problems. Dynamic multiswarm particle swarm optimizer is employed to optimize the necessary parameters for these curves. The results show that Bezier curve is the most suitable curve for producing path for the certain path planning problems discussed in this paper. Safety criterion is considered as a constrained condition. A new constraint handling method is proposed and compared with other two constraint handling methods. The results show that the new method has a better characteristic to improve the performance of algorithm.
This study proposes a new method for the detection of local invariant features with contour. This method differs from traditional methods that use image intensity. Image contours can be extracted stably with changes i...
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This study proposes a new method for the detection of local invariant features with contour. This method differs from traditional methods that use image intensity. Image contours can be extracted stably with changes in viewpoint, scale, illumination and other factors. The proposed algorithm first extracts the stable corner from the contour, then it fits the supporting region of the contour near the corner to an angle, and uses its bisector as the direction of the feature. Next, it searches the contour for the tangent point in the direction of the angle bisector. Finally, with the corner as the centre, and in combination with the tangent point and the feature direction, an elliptic invariant region is constructed. The feasibility of the algorithm was verified experimentally by comparing its repetition rate. Test images obtained from actual scenes include several types of transformations, such as rotation, scaling, affinity, illumination and noise. The results of the experiment show the feasibility of the proposed method for use in local invariant features detection.
Single-objection function cannot describe the characteristics of the complicated hydrologic system. Consequently, it stands to reason that multiobjective functions are needed for calibration of hydrologic model. The m...
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Single-objection function cannot describe the characteristics of the complicated hydrologic system. Consequently, it stands to reason that multiobjective functions are needed for calibration of hydrologic model. The multiobjective algorithms based on the theory of nondominate are employed to solve this multiobjective optimal problem. In this paper, a novel multiobjective optimization method based on differential evolution with adaptive Cauchy mutation and Chaos searching (MODE-CMCS) is proposed to optimize the daily streamflow forecasting model. Besides, to enhance the diversity performance of Pareto solutions, a more precise crowd distance assigner is presented in this paper. Furthermore, the traditional generalized spread metric (SP) is sensitive with the size of Pareto set. A novel diversity performance metric, which is independent of Pareto set size, is put forward in this research. The efficacy of the new algorithmMODE-CMCS is compared with the nondominated sorting genetic algorithm II (NSGA-II) on a daily streamflow forecasting model based on support vector machine (SVM). The results verify that the performance of MODE-CMCS is superior to the NSGA-II for automatic calibration of hydrologic model.
Imperfect preventive maintenance (PM) activities are very common in industrial systems. For condition-based maintenance (CBM), it is necessary to model the failure likelihood of systems subject to imperfect PM activit...
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Imperfect preventive maintenance (PM) activities are very common in industrial systems. For condition-based maintenance (CBM), it is necessary to model the failure likelihood of systems subject to imperfect PM activities. In this paper, the models in the field of survival analysis are introduced into CBM. Namely, the generalized accelerated failure time (AFT) frailty model is investigated to model the failure likelihood of industrial systems. Further, on the basis of the traditional maximum likelihood (ML) estimation and expectation maximization (EM) algorithm, the hybrid ML-EM algorithm is investigated for the estimation of parameters. The hybrid iterative estimation procedure is analyzed in detail. In the evaluation experiment, the generated data of a typical degradation model are verified to be appropriate for the real industrial processes with imperfect PM activities. The estimates of the model parameters are calculated using the training data. Then, the performance of the model is analyzed through the prediction of remaining useful life (RUL) using the testing data. Finally, comparison between the results of the proposed model and the existing model verifies the effectiveness of the generalized AFT frailty model.
This paper studies in detail the background and implementation of a teaching-learning based optimization (TLBO) algorithm with differential operator for optimization task of a few mechanical components, which are esse...
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This paper studies in detail the background and implementation of a teaching-learning based optimization (TLBO) algorithm with differential operator for optimization task of a few mechanical components, which are essential for most of the mechanical engineering applications. Like most of the other heuristic techniques, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. A differential operator is incorporated into the TLBO for effective search of better solutions. To validate the effectiveness of the proposed method, three typical optimization problems are considered in this research: firstly, to optimize the weight in a belt-pulley drive, secondly, to optimize the volume in a closed coil helical spring, and finally to optimize the weight in a hollow shaft. have been demonstrated. Simulation result on the optimization (mechanical components) problems reveals the ability of the proposed methodology to find better optimal solutions compared to other optimization algorithms.
A novel feature selection algorithm is proposed, which is related to the Discriminative Common Vector Approach (DCVA) utilized as a means to reduce the computational complexity of the facial recognition problem. The r...
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A novel feature selection algorithm is proposed, which is related to the Discriminative Common Vector Approach (DCVA) utilized as a means to reduce the computational complexity of the facial recognition problem. The recognition performance of the selected features is tested with DCVA and well known subspace methods over AR and YALE face databases. Moreover, the scheme indicates that important facial parts like eyes, eyebrows, noses, and lips must be kept for recognition purposes while eliminating the pixels in cheek, chin, and forehead areas. This additional knowledge comes out in the form of T-shaped and elliptical face masks used to specify the region of interest (ROI). Hence, besides the excellent dimensionality reduction given by the use of the DCVA technique, there is an intelligent use of the original database that provides superior results even in the presence of an occlusion as it is the case when the facial images have scarves. (C) 2014 Elsevier Ltd. All rights reserved.
The random displacement of magnetic field lines in the presence of magnetic turbulence in plasmas is investigated from first principles. A two-component (slab/two-dimensional composite) model for the turbulence spectr...
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The random displacement of magnetic field lines in the presence of magnetic turbulence in plasmas is investigated from first principles. A two-component (slab/two-dimensional composite) model for the turbulence spectrum is employes. An analytical investigation of the asymptotic behavior of the field-line mean square displacement (FL-MSD) is carried out. It is shown that the magnetic field lines behave superdifusively for every large values of the position variable z, since the FL-MSD sigma varies as sigma similar to z(4/3). An intermediate diffusive regime may also possible exist for finite values of z under conditions which are explicitly determined in terms of the intrinsic turbulent plasma parameters. The superdiffusie asymptotic result is confirmed numerically via an iterative algorithm. The relevance to previous resuslts is discussed.
The nonlocal means algorithm is widely used in image denoising, but this algorithm does not work well for high-intensity noise. To overcome this shortcoming, we establish a coupled iterative nonlocal means model in th...
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The nonlocal means algorithm is widely used in image denoising, but this algorithm does not work well for high-intensity noise. To overcome this shortcoming, we establish a coupled iterative nonlocal means model in this paper. Considering the computation complexity of the new model, we realize it by using multiscale wavelet transform and propose an asymptotic nonlocal filtering algorithm which can reduce the influence of noise on similarity estimation and computation complexity. Moreover, we build a new nonlocal weight function based on the structure similarity index. Simulation results indicate that the proposed approach cannot only remove the noise but also preserve the structure of image and has good visual effects, especially for highly degenerated images.
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