Papers by Keyword: Simulated Annealing (SA)

Abstract: To maximize the fundamental frequency of composite laminates, a hybrid optimization algorithm which combines the respective merits of the genetic algorithm and the simulated annealing algorithm is adopted. This hybrid algorithm also incorporates adaptive mechanisms designed to adjust the probabilities of the cross-over and mutation operators. Then, this algorithm is applied to optimize the fiber angle of each layer of a composite laminate such that its fundamental natural frequency is maximized. The results indicate that this hybrid optimization algorithm could quickly find the optimal fiber angles and maximize the fundamental frequency, even under complicated choices of fiber angle and boundary conditions.

Abstract: The permutation flow shop problem (PFSP) is an NP-hard permutation sequencing scheduling problem, many meta-heuristics based schemes have been proposed for finding near optimal solutions. A simple insertion simulated annealing (SISA) scheme consisting of two phases is proposed for solving PFSP. First, to reduce the complexity, a simple insertion local search is conducted for constructing the solution. Second, to ensure continuous exploration in the search space, two non-decreasing temperature control mechanisms named Heating SA and Steady SA are introduced in a simulated annealing (SA) procedure. The Heating SA increases the exploration search ability and the Steady SA enhances the exploitation search ability. The most important feature of SISA is its simple implementation and low computation time complexity. Experimental results are compared with other state-of-the-art algorithms and reveal that SISA is able to efficiently yield good permutation schedule.

Abstract: To solve the problem of standard particle swarm optimization (PSO) easy turn to premature convergence and poor ability in local search, this paper present a hybrid particle swarm optimization algorithm merging simulated annealing (SA) and mountain-climb. During the running time, the algorithm use the pso to find the global optimal position quickly, take advantage of the Gaussian mutation and mountain-climb strategy to enhance local search ability, and combine with SA to strengthen the population diversity to enable particles to escape from local minima. Test results on several typical test functions show that this new algorithm has a significant improve in searching ability and effectively overcome the premature convergence problem.

Abstract: Surface integrity, such as surface roughness and residual stress, is an aspect of surface quality on machined parts. Residual stress in the machined surface and subsurface is affected by materials, machining conditions, and tool geometry. These residual stresses could affect the service qualify and component life significantly. Residual stress can be determined by empirical or numerical experiments for selected configurations, even if both are expensive procedures. This paper presents a hybrid neural network that is trained using Simulated Annealing (SA) and Levenberg-Marquardt Algorithm (LM) in order to predict the values of residual stresses in cutting and radial direction after the MQL face turning process accurately. To verify the performance of the proposed approach, the predicted results are compared with the results obtained by training an ANN using SA and LM separately. The results have shown that the hybrid neural network outperforms SA and LM in predicting machining induced surface integrity that is critical to determine the fatigue life of the components.

Abstract: The image registration is a very important task in image processing. In the field of medical imaging, it is used to compare the anatomical structures of two or more images taken at different time to track for example the evolution of a disease. Intensity-based techniques are widely used in the multi-modal registration. To have the best registration, a cost function expressing the similarity between these images is maximized. The registration problem is reduced to the optimization of a cost function. We propose to use neighborhood meta-heuristics (tabu search, simulated annealing) and a meta-heuristic population (genetic algorithms). An evaluation step is necessary to estimate the quality of registration obtained. In this paper we present some results of medical image registration

Abstract: Power system stabilizers (PSS) are usually used in power system plants to damp out power system oscillations. In this paper, coordinated and robust tuning procedures by Hybridization technique are used. This hybridization is based on multiobjective functions using stochastic (GA) methods and deterministic methods (gradient) and even between themselves stochastic methods (GA-SA). To validate the effectiveness of this tuning approach in enhancing the stability of power systems, modal analysis and nonlinear simulations have been carried out on a multimachine power system.

Abstract: Runtime verification checks whether one system execution conforms to a group of specific requirements. One of the many problems concerned is how to lower the costly instrumentation overhead, because the performance is as important as correctness. The solutions to cope with the problem include static checking, and hardware based instrumentation. What makes a difference to our approach is that it takes into account the modular structure of the observed run, and our approach makes a tradeoff between observability and cost by means of simulated annealing algorithm. The paper gives a definition of instrumentation observability in detail. The simulated annealing approach works out an optimized instructive instrumentation solution containing part of all the observing points. An experiment is conducted on the LwIP protocol stack to prove the effect of our observing strategy.

Abstract: In order to control an unmanned helicopter accurately and reliably, it is necessary to have a precise mathematical model of its dynamics. This paper presents a new timedomain identification method and process for full state space model of small-scale unmanned helicopters. The identification method is called ISAcwPEM (Improved Simulated Annealing combined with Prediction Error Method), which is not sensitive to initial point selection and doesn’t require frequency-sweeping inputs. Firstly, the primary parameters to be identified are selected by model sensitivity analysis. After that, the improved simulated annealing algorithm runs in a distributed computing platform to figure out a 13-order state space model of the SJTU T-REX700E small-scale unmanned helicopter (consisting of a cruise modal and a hover modal). Then the iterative Prediction Error Method (PEM) is used to optimize the model. In addition, the time-delay term and the trim term are estimated and added to the model. Finally, the effectiveness of the identification method is well validated by real outdoor flight experimental results.

Abstract: The passage aims at solving the problems resulted from the optimized process of Particle Swarm Optimization (PSO), which might reduce the population diversity, cause the algorithm to convergence too early, etc. A whole new mutable simulated annealing particle swarm optimization is proposed based on the combine of the simulated annealing mechanism and mutation. This new algorithm substitutes the Metropolis criterion in the simulated annealing mechanism for mutagenic factors in the process of mutation, which both ensures the diversity of the particle swarm, and ameliorates the quality of the swarm, so that this algorithm would convergence to the global optimum. According to the result of simulated analysis, this hybrid algorithm maintains the simplicity of the particle swarm optimization, improves its capability of global optimization, and finally accelerates the convergence and enhances the precision of this algorithm.

Abstract: High-resolution satellite remote sensing image are mostly used for accurate updating of GIS data. As the primary GIS data, urban roads on the image show the rich geometric features and radiation characteristics, that edge detection and grouping becoming an important way to solve the road extraction. However, edge elements obtained from images are always discontinuous for interference of noise and weak contrast between road and background. What more, vehicles, plant, buildings and shadow blocking results in weak grouping relation of elements. In processing, insignificant candidate road may be weeded out as noise and lead to failure road extraction. This paper presents a semi-automated extraction method for low contrast road basing on statistical grouping of orientation texture feature. Multi-direction and multi-scale Gabor filters are employed to detect directions of road texture. Then same direction pixels are grouped under constraining of rectangle template and generate road base elements. Finally, simulated annealing algorithm is used to optimize elements connection. Experiment results show that proposed method was effective in accurate extraction of low contrast road.