Showing papers on "Simulated annealing published in 2006"

DASH: a program for crystal structure determination from powder diffraction data

Abstract: DASH is a user-friendly graphical-user-interface-driven computer program for solving crystal structures from X-ray powder diffraction data, optimized for molecular structures. Algorithms for multiple peak fitting, unit-cell indexing and space-group determination are included as part of the program. Molecular models can be read in a number of formats and automatically converted to Z-matrices in which flexible torsion angles are automatically identified. Simulated annealing is used to search for the global minimum in the space that describes the agreement between observed and calculated structure factors. The simulated annealing process is very fast, which in part is due to the use of correlated integrated intensities rather than the full powder pattern. Automatic minimization of the structures obtained by simulated annealing and automatic overlay of solutions assist in assessing the reproducibility of the best solution, and therefore in determining the likelihood that the global minimum has been obtained.

A survey of simulated annealing as a tool for single and multiobjective optimization

Abstract: This paper presents a comprehensive review of simulated annealing (SA)-based optimization algorithms. SA-based algorithms solve single and multiobjective optimization problems, where a desired global minimum/maximum is hidden among many local minima/maxima. Three single objective optimization algorithms (SA, SA with tabu search and CSA) and five multiobjective optimization algorithms (SMOSA, UMOSA, PSA, WDMOSA and PDMOSA) based on SA have been presented. The algorithms are briefly discussed and are compared. The key step of SA is probability calculation, which involves building the annealing schedule. Annealing schedule is discussed briefly. Computational results and suggestions to improve the performance of SA-based multiobjective algorithms are presented. Finally, future research in the area of SA is suggested.

Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting

Abstract: Accurate forecasting of electricity load has been one of the most important issues in the electricity industry. Recently, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. Because of the general nonlinear mapping capabilities of forecasting, artificial neural networks have played a crucial role in forecasting electricity load. Support vector machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. However, SVMs have rarely been applied to forecast electricity load. This investigation elucidates the feasibility of using SVMs to forecast electricity load. Moreover, simulated annealing (SA) algorithms were employed to choose the parameters of a SVM model. Subsequently, examples of electricity load data from Taiwan were used to illustrate the proposed SVMSA (support vector machines with simulated annealing) model. The empirical results reveal that the proposed model outperforms the other two models, namely the autoregressive integrated moving average (ARIMA) model and the general regression neural networks (GRNN) model. Consequently, the SVMSA model provides a promising alternative for forecasting electricity load.

Unit commitment by an enhanced simulated annealing algorithm

Abstract: A new simulated annealing (SA) algorithm combined with a dynamic economic dispatch method has been developed for solving the short-term unit commitment (UC) problem. SA is used for the scheduling of the generating units, while a dynamic economic dispatch method is applied incorporating the ramp rate constraints in the solution of the UC problem. New rules concerning the tuning of the control parameters of the SA algorithm are proposed. Three alternative mechanisms for generating feasible trial solutions in the neighborhood of the current one, contributing to the reduction of the required CPU time, are also presented. The ramp rates are taken into account by performing either a backward or a forward sequence of conventional economic dispatches with modified limits on the generating units. The proposed algorithm is considerably fast and provides feasible near-optimal solutions. Numerical simulations have proved the effectiveness of the proposed algorithm in solving large UC problems within a reasonable execution time.

Derivative-Free Filter Simulated Annealing Method for Constrained Continuous Global Optimization

Abstract: In this paper, a simulated-annealing-based method called Filter Simulated Annealing (FSA) method is proposed to deal with the constrained global optimization problem. The considered problem is reformulated so as to take the form of optimizing two functions, the objective function and the constraint violation function. Then, the FSA method is applied to solve the reformulated problem. The FSA method invokes a multi-start diversification scheme in order to achieve an efficient exploration process. To deal with the considered problem, a filter-set-based procedure is built in the FSA structure. Finally, an intensification scheme is applied as a final stage of the proposed method in order to overcome the slow convergence of SA-based methods. The computational results obtained by the FSA method are promising and show a superior performance of the proposed method, which is a point-to-point method, against population-based methods.

Accuracy-Guaranteed Bit-Width Optimization

Abstract: An automated static approach for optimizing bit widths of fixed-point feedforward designs with guaranteed accuracy, called MiniBit, is presented. Methods to minimize both the integer and fraction parts of fixed-point signals with the aim of minimizing the circuit area are described. For range analysis, the technique in this paper identifies the number of integer bits necessary to meet range requirements. For precision analysis, a semianalytical approach with analytical error models in conjunction with adaptive simulated annealing is employed to optimize the number of fraction bits. The analytical models make it possible to guarantee overflow/underflow protection and numerical accuracy for all inputs over the user-specified input intervals. Using a stream compiler for field-programmable gate arrays (FPGAs), the approach in this paper is demonstrated with polynomial approximation, RGB-to-YCbCr conversion, matrix multiplication, B-splines, and discrete cosine transform placed and routed on a Xilinx Virtex-4 FPGA. Improvements for a given design reduce the area and the latency by up to 26% and 12%, respectively, over a design using optimum uniform fraction bit widths. Studies show that MiniBit-optimized designs are within 1% of the area produced from the integer linear programming approach

Hybrid discrete particle swarm optimization algorithm for capacitated vehicle routing problem

Abstract: Capacitated vehicle routing problem (CVRP) is an NP-hard problem. For large-scale problems, it is quite difficult to achieve an optimal solution with traditional optimization methods due to the high computational complexity. A new hybrid ap- proximation algorithm is developed in this work to solve the problem. In the hybrid algorithm, discrete particle swarm optimiza- tion (DPSO) combines global search and local search to search for the optimal results and simulated annealing (SA) uses certain probability to avoid being trapped in a local optimum. The computational study showed that the proposed algorithm is a feasible and effective approach for capacitated vehicle routing problem, especially for large scale problems.

Simulated Annealing Technique for Dynamic Economic Dispatch

Abstract: Dynamic economic dispatch (DED) is one of the main functions of power system operation and control. It determines the optimal operation of units with predicted load demands over a certain period of time with an objective to minimize total production cost while the system is operating within its ramp rate limits. This paper presents DED based on a simulated annealing (SA) technique for the determination of the global or near global optimum dispatch solution. In the present case, load balance constraints, operating limits, valve point loading, ramp constraints, and network losses using loss coefficients are incorporated. Numerical results for a sample test system have been presented to demonstrate the performance and applicability of the proposed method.

Fast simulated annealing inversion of surface waves on pavement using phase-velocity spectra

Abstract: The conventional inversion of surface waves depends on modal identification of measured dispersion curves, which can be ambiguous. It is possible to avoid mode-number identification and extraction by inverting the complete phase-velocity spectrum obtained from a multichannel record. We use the fast simulated annealing (FSA) global search algorithm to minimize the difference between the measured phase-velocity spectrum and that calculated from a theoretical layer model, including the field setup geometry. Results show that this algorithm can help one avoid getting trapped in local minima while searching for the best-matching layer model. The entire procedure is demonstrated on synthetic and field data for asphalt pavement. The viscoelastic properties of the top asphalt layer are taken into account, and the inverted asphalt stiffness as a function of frequency compares well with laboratory tests on core samples. The thickness and shear-wave velocity of the deeper embedded layers are resolved within 10% deviation from those values measured separately during pavement construction. The proposed method may be equally applicable to normal soil site investigation and in the field of ultrasonic testing of materials.

Application of a fuzzy neural network combined with a chaos genetic algorithm and simulated annealing to short-term load forecasting

Abstract: A fuzzy neural network combined with a chaos-search genetic algorithm (CGA) and simulated annealing (SA), hereafter called the FCS method, or simply the FCS, applied to short-term power-system load forecasting as a sample test is proposed in this paper. A fuzzy hyperrectangular composite neural network (FHCNN) is adopted for the initial load forecasting. An integrated CGA and fuzzy system (CGF) and SA is then used to find the optimal FHCNN parameters instead of the ones with the back propagation method. The CGF method will generate a set of parameters for a feasible solution. The CGF method holds good global search capability but poor local search ability. On the contrary, the SA method possesses a good local optimal search capability. We hence propose in this paper to combine the two methods to exploit their advantages and, furthermore, to eliminate the known downside of the traditional artificial neural network. The proposed FCS is next applied to power-system load forecasting as a sample test, which demonstrates an encouraging degree of accuracy superior to other commonly used forecasting methods available. The forecasting results are tabulated and partially converted into bar charts for evaluation and clear comparisons.

Spatial sampling design for prediction with estimated parameters

Abstract: We study spatial sampling design for prediction of stationary isotropic Gaussian processes with estimated parameters of the covariance function. The key issue is how to incorporate the parameter uncertainty into design criteria to correctly represent the uncertainty in prediction. Several possible design criteria are discussed that incorporate the parameter uncertainty. A simulated annealing algorithm is employed to search for the optimal design of small sample size and a two-step algorithm is proposed for moderately large sample sizes. Simulation results are presented for the Matern class of covariance functions. An example of redesigning the air monitoring network in EPA Region 5 for monitoring sulfur dioxide is given to illustrate the possible differences our proposed design criterion can make in practice.

Optimal Power Flow by Improved Evolutionary Programming

Abstract: This article proposes an improved evolutionary programming (IEP) for solving optimal power flow (OPF) with nonsmooth and nonconvex generator fuel cost curves. Initially, the whole population is divided into multiple subpopulations, which are used to perform the parallel search in divided solution space. IEP includes Gaussian and Cauchy mutation operators in different subpopulations to enhance the search diversity, selection operators with probabilistic updating strategy to avoid entrapping in local optimum, and reassignment operator for every subpopulation to exchange search information. The proposed IEP was tested on the IEEE 30 bus system with three different types of generator fuel cost curves. It is shown that IEP total generator fuel cost is less expensive than those of evolutionary programming, tabu search, hybrid tabu search and simulated annealing, and improved tabu search, leading to substantial generator fuel cost savings. Moreover, IEP can easily facilitate parallel implementation to reduce the...

Search Based Approaches to Component Selection and Prioritization for the Next Release Problem

Abstract: This paper addresses the problem of determining the next set of releases in the course of software evolution. It formulates both ranking and selection of candidate software components as a series of feature subset selection problems to which search based software engineering can be applied. The approach is automated using greedy and simulated annealing algorithms and evaluated using a set of software components from the component base of a large telecommunications organisation. The results are compared to those obtained by a panel of (human) experts. The results show that the two automated approaches convincingly outperform the expert judgment approach.

Using a Simulated Annealing Algorithm to Solve the Transit Route Network Design Problem

Abstract: This paper uses a simulated annealing algorithm to solve the optimal bus transit route network design problem (BTRNDP) at the distribution node level. A multiobjective nonlinear mixed integer model is formulated for the BTRNDP. The proposed solution framework consists of three main components: An initial candidate route set generation procedure that generates all feasible routes incorporating practical bus transit industry guidelines; and a network analysis procedure that assigns transit trips, determines service frequencies, and computes performance measures; and a simulated annealing procedure that combines these two parts, guides the candidate solution generation process and selects an optimal set of routes from the huge solution space. Three experimental networks are successfully tested as a pilot study. A genetic algorithm is also used as a benchmark to measure the quality of the simulated annealing algorithm. The presented numerical results clearly indicate that the simulated annealing outperforms the genetic algorithm in most cases using the example networks. Sensitivity analyses are performed and related characteristics and tradeoffs underlying the BTRNDP are also discussed.

Unit Commitment by an Enhanced Simulated Annealing Algorithm

Abstract: A new simulated annealing (SA) algorithm combined with a dynamic economic dispatch method has been developed for solving the short-term unit commitment (UC) problem. SA is used for the scheduling of the generating units, while a dynamic economic dispatch method is applied incorporating the ramp rate constraints in the solution of the UC problem. New rules concerning the tuning of the control parameters of the SA algorithm are proposed. Three alternative mechanisms for generating feasible trial solutions in the neighborhood of the current one, contributing to the reduction of the required CPU time, are also presented. The ramp rates are taken into account by performing either a backward or a forward sequence of conventional economic dispatches with modified limits on the generating units. The proposed algorithm is considerably fast and provides feasible near-optimal solutions. Numerical simulations have proved the effectiveness of the proposed algorithm in solving large UC problems within a reasonable execution time

A modified real GA for the sparse linear array synthesis with multiple constraints

Abstract: This paper describes a modified real genetic algorithm (MGA) for the synthesis of sparse linear arrays. The MGA has been utilized to optimize the element positions to reduce the peak sidelobe level (PSLL) of the array. And here the multiple optimization constraints include the number of elements, the aperture and the minimum element spacing. Unlike standard GA using fixed corresponding relationship between the gene variables and their coding, the MGA utilized the coding resetting of gene variables to avoid infeasible solution during the optimization process. Also, the proposed approach has reduced the size of the searching area of the GA by means of indirect description of individual. The simulated results confirming the great efficiency and the robustness of this algorithm are provided in this paper.

An Optimization Methodology for Neural Network Weights and Architectures

Abstract: This paper introduces a methodology for neural network global optimization. The aim is the simultaneous optimization of multilayer perceptron (MLP) network weights and architectures, in order to generate topologies with few connections and high classification performance for any data sets. The approach combines the advantages of simulated annealing, tabu search and the backpropagation training algorithm in order to generate an automatic process for producing networks with high classification performance and low complexity. Experimental results obtained with four classification problems and one prediction problem has shown to be better than those obtained by the most commonly used optimization techniques

An effective hybrid algorithm for university course timetabling

Abstract: The university course timetabling problem is an optimisation problem in which a set of events has to be scheduled in timeslots and located in suitable rooms. Recently, a set of benchmark instances was introduced and used for an `International Timetabling Competition' to which 24 algorithms were submitted by various research groups active in the field of timetabling. We describe and analyse a hybrid metaheuristic algorithm which was developed under the very same rules and deadlines imposed by the competition and outperformed the official winner. It combines various construction heuristics, tabu search, variable neighbourhood descent and simulated annealing. Due to the complexity of developing hybrid metaheuristics, we strongly relied on an experimental methodology for configuring the algorithms as well as for choosing proper parameter settings. In particular, we used racing procedures that allow an automatic or semi-automatic configuration of algorithms with a good save in time. Our successful example shows that the systematic design of hybrid algorithms through an experimental methodology leads to high performing algorithms for hard combinatorial optimisation problems.