Algorithm for Increasing the Speed of Evolutionary Optimization and its Accuracy in Multi-objective Problems

Optimization algorithms are important tools for the solution of combinatorial management problems. Nowadays, many of those problems are addressed by using evolutionary algorithms (EAs) that move toward a near-optimal solution by repetitive simulations. Sometimes, such extensive simulations are not possible or are costly and time-consuming. Thus, in this study a method based on artificial neural networks (ANN) is proposed to reduce the number of simulations required in EAs. Specifically, an ANN simulator is used to reduce the number of simulations by the main simulator. The ANN is trained and updated only for required areas in the decision space. Performance of the proposed method is examined by integrating it with the non-dominated sorting genetic algorithm (NSGAII) in multi-objective problems. In terms of density and optimality of the Pareto front, the hybrid NSGAII-ANN is able to extract the Pareto front with much less simulation time compared to the sole use of the NSGAII algorithm. The proposed NSGAII-ANN methodology was examined using three standard test problems (FON, KUR, and ZDT1) and one real-world problem. The latter addresses the operation of a reservoir with two objectives (meeting demand and flood control). Thus, based on this study, use of the NSGAII-ANN integrative algorithm in problems with time-consuming simulators reduces the required time for optimization up to 50 times. Results of the real-world problem, despite lower computational-time requirements, show a performance similar to that achieved in the aforementioned test problems.     

Evaluation of suspended load transport rate using transport formulas and artificial neural network models （Case study： Chelchay Catchment）＊

Accurate estimation of sediment load or transport rate is very important to a wide range of water resources projects. This study was undertaken to determine the most appropriate model to predict suspended load in the Chelchay Watershed, northeast of Iran. In total, 59 data series were collected from four gravel bed-rivers and a sand bed river and two depth integrating suspended load samplers to evaluate nine suspended load formulas and feed forward backpropagation Artificial Neural Network (ANN) structures. Although the Chang formula with higher correlation coefficient (r = 0.69) and lower Root Mean Square Error (RMSE = 0.013) is the best suspended load predictor among the nine studied formulas, the ANN models significantly outperform traditional suspended load formulas and show their superior performance for all statistical parameters. Among different ANN structures two models including 4 inputs, 4 hidden and one output neurons, and 4 inputs, 4 and one hidden and one output neurons provide the best simulation with the RMSE values of 0.0009 and 0.001, respectively.     

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

Reduction of Noise from Disc Brake Systems Using Composite Friction Materials Containing Thermoplastic Elastomers (TPEs)

Attempts have been made for the first time to prepare a friction material with the characteristic of thermal sensitive modulus, by the inclusion of thermoplastic elastomers (TPE) as viscoelastic polymeric materials into the formulation in order to the increase the damping behavior of the cured friction material. Styrene–butadiene–styrene (SBS), styrene–ethylene–butylene–styrene (SEBS) and nitrile rubber/polyvinyl chloride (NBR/PVC) blend system were used as TPE materials. In order to evaluate the viscoelastic parameters such as loss factor (tan δ) and storage modulus (E′) for the friction material, dynamic mechanical analyzer (DMA) were used. Natural frequencies and mode shapes of friction material and brake disc were determined by modal analysis. However, NBR/PVC and SEBS were found to be much more effective in damping behavior. The results from this comparative study suggest that the damping characteristics of commercial friction materials can be strongly affected by the TPE ingredients. This investigation also confirmed that the specimens with high TPE content had low noise propensity.     

Flow resistance in a compound gravel-bed bend

In this paper, the effect of a gravel-bed in a compound bend (similar to sinusoidal top view) of a natural river (Zayandehrud River flowing through Isfahan, Iran) has been investigated for flow resistance analysis, measuring the velocity with a micro current meter. The data were analysed and the following observations were made. In a compound bend, the law of the wall can be valid for up to 66% of the flow depth from the bed. The parabolic law is the most effective method for the determination of shear velocity. Based on the existing criteria for verifying the equilibrium boundary layer, the flow cannot be in equilibrium. The shear stress distribution and the sediment transport parameters have considerable influence on resistance to flow. Froude number and the flow depth relative to the representative gravel size have little effect on the flow resistance estimation.     

Parametric meshless Galerkin method

In meshless methods, generation of meshless shape functions is usually a complicated and time‐consuming task. In this paper, a new meshless method called parametric meshless Galerkin method (PMGM) is presented. In this method, meshless shape functions are constructed on meshless parametric domains (MPD), before running to solve the problem. For modelling the new problems, MPDs are mapped to the physical space. Therefore the shape functions constructing time can be saved. Mapping is simply performed by defining a linear function. Also, the integration grids are defined in the MPD and it is not necessary to create background integration grids separately for each problem. The method is described for two‐dimensional problems, but it can be applied to three‐dimensional problems in the same way. It is shown that using the PMGM, a time saving as much as 21% is achieved with respect to the element‐free Galerkin method for the numerical examples and the obtained results show efficiency and convergence of the method. Copyright © 2005 John Wiley & Sons, Ltd.     

Role of laser sensor systems in automation and flexible manufacturing

New industries and production plants require a flexible system, which is capable of picking up objects of various shapes, weights, and colors with arbitrary position and orientation. Such a system also needs recognition and guiding sub-systems. The recognition system includes target function for the recognition sub-system and relation between object characteristics and recognition target. The laser sensor system can be used for such object recognition. Wire-based telemetry and control systems can cause many problems in shop floors and factories, and so there has been a strong growth of interest in wireless guidance like vehicles equipped with laser guiding and navigation systems. For the continuous measurements of parameters such as temperature, etc. optical laser sensor technology seems to become more applicable at this stage. This article describes the operational principles and the use of the most advanced laser sensor systems for quantity measurements, guiding, navigation, pattern recognition, and vision systems for inspection purposes. A variety of laser-based sensors, which can be used as sensing devices in manufacturing, and production technology, are described in this study. Adaptive cruise control systems that can be used in automobile industry to monitor distance and speed are described in this report. As a typical example, the principal operation of a laser guided mobile robot using a laser navigation system is also described.     

The availability of source code in relation to timely response to security vulnerabilities

Once a vulnerability has been found in an application or service that runs on a computer connected to the Internet, fixing that exploit in a timely fashion is of the utmost importance. There are two parts to fixing vulnerability: a party acting on behalf of the application's vendor gives instructions to fix it or makes a patch available that can be downloaded; then someone using that information fixes the computer or application in question. This paper considers the effects of proprietary software versus non-proprietary software in determining the speed with which a security fix is made available, since this can minimize the amount of time that the computer system remains vulnerable.     

A probabilistic framework for specular shape-from-shading

One of the problems that hinders conventional methods for shape-from-shading is the presence of local specularities which may be misidentified as high curvature surface features. In this paper we address the problem of estimating the proportions of Lambertian and specular reflection components in order to improve the quality of surface normal information recoverable using shape-from-shading. The framework for our study is provided by the iterated conditional modes algorithm. We develop a maximum a posteriori probability (MAP) estimation method for estimating the mixing proportions for Lambertian and specular reflectance, and also, for recovering local surface normals. The MAP estimation scheme has two model ingredients. First, there are separate conditional measurement densities which describe the distributions of surface normal directions for the Lambertian and specular reflectance components. We experimentally compare three different models for the specular component. The second ingredient is a smoothness prior which models the distribution of surface normal directions over local image regions. We demonstrate the utility of method on real-world data. Ground truth data is provided by imagery obtained with crossed polaroid filters. This reveals not only that the method accurately estimates the proportion of specular reflection, but that it also results in good surface normal reconstruction in the proximity of specular highlights.