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The paper introduces a bio-inspired approach to solve the route allocation problem (RAP) in the transportation networks. The approach extends a well-known meta-heuristics algorithm with the real life constraints that are dealt with in the scheduling process (i.e. the uniform distribution of routes diversity for vehicles, the average distance travelled in a month, the driver’s rest between subsequent trips etc.). The paper is focusing on the engineering aspects of employing bio-inspired algorithms (which proved to have near-optimal results for toy-like problems) to a real-life application domain. The approach proved to be capable of preserving the software components (agents) to the complexity and dynamics of the situation when the RAP requires incremental extensions of constraints to reflect the traffic conditions in the transportation network. [ABSTRACT FROM AUTHOR]

This paper summarizes the collaborative work done by CENER, Fraunhofer ISE and CRS4 within the scope of the STAGE-STE project related to heliostat field generation algorithms and their application to small heliostats (<10 m²). Radially staggered heliostat field layouts have been commonly applied to real solar fields with known results. However, their use in conjunction with small heliostats or the use of brand new ways for heliostat allocation, such natural pattern based algorithms, is still unknown. Herein, the most promising heliostat field generation algorithms are studied and compared, in annual optical efficiency terms, for different scenarios. These efficiencies correspond to the best heliostat layouts found by the optimization process that each generation algorithm can create, for three scenarios. Results show that the fields from the selected algorithms lead to similar optical efficiencies. The slight differences are not enough evidence to nominate one of the algorithms as the best choice, taking into account the inherent error of the simulation tools, the optimization process and further requirements needed in commercial applications (e.g. access paths) not coped in this study. [ABSTRACT FROM AUTHOR]

This paper presents several approaches for preform shape optimization in 3D forging. The process simulation is carried out using the FORGE3® finite element software, and the optimization problem regards the shape of initial axisymmetrical preforms. Several objective functions are considered, like the forging energy, the forging force or a surface defect criterion. Both deterministic and stochastic optimization algorithms are tested for 3D applications. The deterministic approach uses the sensitivity analysis that provides the gradient of the objective function. It is obtained by the adjoint-state method and semi-analytical differentiation. The study of stochastic approaches aims at comparing genetic algorithms and evolution strategies. Numerical results show the feasibility of such approaches, i.e. the achieving of satisfactory solutions within a limited number of 3D simulations, less than fifty. For a more industrial problem, the forging of a gear, encouraging optimization results are obtained. © 2004 American Institute of Physics [ABSTRACT FROM AUTHOR]

Nondestructive testing methods have been routinely, designed, evaluated and optimized using simulation models developed using various computational techniques. The simulated signal using computational model differs from the true signal in that the signal does not simulated experimental noise. In order to use the computational models more effectively for signal processing algorithm development, experimental noise should be injected in the simulated signals. Experimental noise PDF (probability density function) can be numerically calculated from measured noise. The experimental signal PDF can then be generated by combining the simulation signal and measurement noise PDF. Sampling from experimental signal distribution is not a straight forward task as the distribution is generally not a standard parametric distribution. This paper presents a method that approximates experimental signal PDF as a mixture of Gaussian densities. Maximum-likelihood estimate of the parameters of Gaussian distributions from a given data set are computed using expectation-maximization (EM) technique. A sampling scheme from the mixture of Gaussian densities is also discussed. The overall algorithm is implemented on eddy current inspection data from steam generator (SG) tubing. [ABSTRACT FROM AUTHOR]

We investigate in this paper the recovery of the local volatility surface in a parametric framework similar to that of Coleman, Li and Verma [1]. The quality of a surface is assessed through a functional which is optimized; the specificity of the approach is to separate the optimization on the parametric space (performed with any suitable optimization algorithm) from the computation of the functional where we use an adjoint formulation similar to that of the optimal control; the procedure can thus incorporate information from any derivative contract compatible with the adjoint approach. The procedure was implemented and was shown to perform satisfactory on real-world data. [ABSTRACT FROM AUTHOR]

Numerical simulation technology and optimization method have been applied in sheet metal forming process to improve design quality and shorten design cycle. While the existence of fluctuation in design variables or operation condition has great influence on the quality. In addition to that, iterative solution in numerical simulation and optimization usually take huge computational time or endure expensive experiment cost In order to eliminate effect of perturbations in design and improve design efficiency, a CAE-based six sigma robust design method is developed in this paper. In the six sigma procedure for sheet metal forming, statistical technology and dual response surface approximate model as well as algorithm of “Design for Six Sigma (DFSS)” are integrated together to perform reliability optimization and robust improvement. A deep drawing process of a rectangular cup is taken as an example to illustrate the method. The optimization solutions show that the proposed optimization procedure not only improves significantly the reliability and robustness of the forming quality, but also increases optimization efficiency with approximate model. © 2005 American Institute of Physics [ABSTRACT FROM AUTHOR]

In order to optimize the cloud computing task scheduling scheme, an improved differential evolution algorithm for cloud computing task scheduling is proposed. Firstly, the cloud computing task scheduling model, according to the model of the fitness function, and then used improved optimization calculation of the fitness function of the evolutionary algorithm, according to the evolution of generation of dynamic selection strategy through dynamic mutation strategy to ensure the global and local search ability. The performance test experiment was carried out in the CloudSim simulation platform, the experimental results show that the improved differential evolution algorithm can reduce the cloud computing task execution time and user cost saving, good implementation of the optimal scheduling of cloud computing tasks. [ABSTRACT FROM AUTHOR]

We have reviewed the simulation modeling problems concerning the technological process of transport support. We have proposed the solutions to optimization problems. An optimal path algorithm has been developed in the Anylogic software. Also, the optimization process of the number of vehicles in the specified conditions has been performed. The results of computer experiments are given. [ABSTRACT FROM AUTHOR]

Genetic Algorithm (GA) is a search method that simulates the process of natural selection and it attempts to find a good solution to some problem by randomly generating a collection of potential solutions to the problem and then manipulating those solutions using genetic operators. Through selection, mutation and re-combination (crossover) operations, better solutions are hopefully generated out of the current set of potential solutions. This process continues until an acceptable solution is found. GA is becoming popular to solve the optimization problems mainly because of its robustness in finding optimal solution and ability to provide near optimal solution close to global optimum. The ELD approach is tested on sample 3-generator system with the load of 24 hours. [ABSTRACT FROM AUTHOR]

ACS algorithms have been used in solving NP-hard and optimization problems in recent years. ACS ant colonies are homogeneous, but natural colonies are not. In this paper, a new ACS algorithm is proposed. It uses heterogeneous ant colonies which are evolved using a new type of genetic algorithm. Experimental results obtained from solving TSP problem, show the superiority of proposed algorithm over classical ACS. [ABSTRACT FROM AUTHOR]

Commonly used standard deterministic procedures are not efficient enough to solve complex optimization problems. The main drawback of the classical optimization techniques is that large number of objective functions may show appearance of local minima. In such case, these methods fail, and it is difficult to localize the global minimum. One of frequently used solution is application of probabilistic methods i.e. Monte Carlo. These procedures, based on random search, are less sensitive to the local minima. The optimization methods, based on the Artificial Neural Network appear a competitive tools in relation to standard sequential or probabilistic optimization algorithms. The objective of the work is Artificial Neural Networks optimization strategy development and application to the minimization problem of anisotropic effect in deformed material. The anisotropic effect observed in deformed material is an undesired phenomenon, which can cause the elimination of thin products where high quality is required. Therefore, it is very important to look for the deformation technology, which minimizes the anisotropy effect. The work deals with the thin cup bulge forming process. The experiment of axisymertrical bulging process includes unbounded expansion of thin walls of cup being formed by superelastic die (Fig. 1). The objective function is based on the final shape of formed element, while the optimization variable is the punch velocity. The anisotropic model of material was implemented to the FEM code and simulations of the deformation process ware performed. The comparison between experimental results and FEM simulations was shown. © 2004 American Institute of Physics [ABSTRACT FROM AUTHOR]

Simulation-driven optimization problems are often used with the aid of metamodels, as the latter provide predicted objective values at a lower computational cost. To further improve the prediction accuracy, an ensemble of metamodel incorporates several metamodels concurrently and aggregates their prediction into a single one. An open question is which types of metamodels should be incorporated, and this is typically not known a-priori, while using a fixed topology may hamper the optimization process. To address this issue, this study proposes a new metamodel-assisted algorithm with dynamic topology adaptation, namely, it autonomously adapts the ensemble topology during the search, and selects the most suitable topology as the search progresses. An extensive performance analysis shows the effectiveness of the proposed algorithm, and highlights the merit of the proposed topology adaptation. [ABSTRACT FROM AUTHOR]

The design of an ultrasonic array and its settings for a specific inspection involves multiple parameters and is the result of a compromise between different requirements and limitations. Parametric studies based on simulation tools are often performed to ensure that the transducer has suitable performances in terms of detection of the defects sought. An automatic optimization tool, based on an evolutionary algorithm driving CIVA's simulation data, is proposed and tested in realistic applications to design inspection parameters (designing, positioning and/or setting the transducer). [ABSTRACT FROM AUTHOR]

Lexicographic optimization based model predictive control is a modified Model Predictive Control algorithm (MPC), which incorporates explicit controlled output priority by solving a series of optimization problems at every iteration in order of priority. We present an application of controlling liquid levels in quadruple cylindrical tanks using lexicographic optimization based MPC and compare the results with conventional MPC approach. The results show the superiority of lexicographic optimization based MPC over conventional MPC in terms of setpoint tracking. Extended Kalman filter is used as the state observer for the simulation results in this work for nonlinear quadruple cylindrical tank system. [ABSTRACT FROM AUTHOR]

A novel physical model and a simulation algorithm capable of predicting electromigration (EM) induced void nucleation and growth in an arbitrary interconnect segment have been developed. EM-induced void nucleation and growth in dual damascene (DD) copper interconnect structures have been predicted with a developed physical model and a simulation algorithm. The incorporation of all important contributions to directed atom migration into the mass balance equation and its solution together with the solution of the corresponding electromagnetics, heat transfer and elasticity problems, in a coupled manner, allows to simulate EM-induced degradation in dual-inlaid copper interconnect segments characterized by different dominant channels for EM-induced mass transport. Model provides a capability for the EM design rules generation/optimization with the physically based simulations. A simulation-based optimization of interconnect architecture, segment geometry, material properties and some of the process parameters can generate on-chip interconnect systems with a high immunity to EM-induced failures. © 2004 American Institute of Physics [ABSTRACT FROM AUTHOR]