Showing papers on "Simulated annealing published in 2008"

A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA

Abstract: This paper describes a simulated annealing based multiobjective optimization algorithm that incorporates the concept of archive in order to provide a set of tradeoff solutions for the problem under consideration. To determine the acceptance probability of a new solution vis-a-vis the current solution, an elaborate procedure is followed that takes into account the domination status of the new solution with the current solution, as well as those in the archive. A measure of the amount of domination between two solutions is also used for this purpose. A complexity analysis of the proposed algorithm is provided. An extensive comparative study of the proposed algorithm with two other existing and well-known multiobjective evolutionary algorithms (MOEAs) demonstrate the effectiveness of the former with respect to five existing performance measures, and several test problems of varying degrees of difficulty. In particular, the proposed algorithm is found to be significantly superior for many objective test problems (e.g., 4, 5, 10, and 15 objective problems), while recent studies have indicated that the Pareto ranking-based MOEAs perform poorly for such problems. In a part of the investigation, comparison of the real-coded version of the proposed algorithm is conducted with a very recent multiobjective simulated annealing algorithm, where the performance of the former is found to be generally superior to that of the latter.

The Traveling Salesman Problem: A Case Study in Local Optimization

Abstract: This is a preliminary version of a chapter that appeared in the book Local Search in Combinatorial Optimization, E H L Aarts and J K Lenstra (eds), John Wiley and Sons, London, 1997, pp 215-310 The traveling salesman problem (TSP) has been an early proving ground for many approaches to combinatorial optimization, including classical local optimization techniques as well as many of the more recent variants on local optimization, such as simulated annealing, tabu search, neural networks, and genetic algorithms This chapter discusses how these various approaches have been adapted to the TSP and evaluates their relative success in this perhaps atypical domain from both a theoretical and an experimental point of view

BEST: Bayesian estimation of species trees under the coalescent model

Abstract: Summary: BEST implements a Bayesian hierarchical model to jointly estimate gene trees and the species tree from multilocus sequences. It provides a new option for estimating species phylogenies within the popular Bayesian phylogenetic program MrBayes. The technique of simulated annealing is adopted along with Metropolis coupling as performed in MrBayes to improve the convergence rate of the Markov Chain Monte Carlo algorithm. Availability: http://www.stat.osu.edu/~dkp/BEST.

Parameter determination of support vector machine and feature selection using simulated annealing approach

Abstract: Support vector machine (SVM) is a novel pattern classification method that is valuable in many applications. Kernel parameter setting in the SVM training process, along with the feature selection, significantly affects classification accuracy. The objective of this study is to obtain the better parameter values while also finding a subset of features that does not degrade the SVM classification accuracy. This study develops a simulated annealing (SA) approach for parameter determination and feature selection in the SVM, termed SA-SVM. To measure the proposed SA-SVM approach, several datasets in UCI machine learning repository are adopted to calculate the classification accuracy rate. The proposed approach was compared with grid search which is a conventional method of performing parameter setting, and various other methods. Experimental results indicate that the classification accuracy rates of the proposed approach exceed those of grid search and other approaches. The SA-SVM is thus useful for parameter determination and feature selection in the SVM.

Practical bayesian optimization

Abstract: Global optimization of non-convex functions over real vector spaces is a problem of widespread theoretical and practical interest. In the past fifty years, research in global optimization has produced many important approaches including Lipschitz optimization, simulated annealing, homotopy methods, genetic algorithms, and Bayesian response-surface methods. This work examines the last of these approaches. The Bayesian response-surface approach to global optimization maintains a posterior model of the function being optimized by combining a prior over functions with accumulating function evaluations. The model is then used to compute which point the method should acquire next in its search for the optimum of the function. Bayesian methods can be some of the most efficient approaches to optimization in terms of the number of function evaluations required, but they have significant drawbacks: Current approaches are needlessly data-inefficient, approximations to the Bayes-optimal acquisition criterion are poorly studied, and current approaches do not take advantage of the small-scale properties of differentiable functions near local optima. This work addresses each of these problems to make Bayesian methods more widely applicable.

Fast simulated annealing

Abstract: Simulated annealing is a stochastic strategy for searching the ground state. A fast simulated annealing (FSA) is a semi‐local search and consists of occasional long jumps. The cooling schedule of FSA algorithm is inversely linear in time which is fast compared with the classical simulated annealing (CSA) which is strictly a local search and requires the cooling schedule to be inversely proportional to the logarithmic function of time. A general D dimensional Cauchy probability for generating the state is given. Proofs for both FSA and CSA are sketched. A double potential well is used to numerically illustrate both schemes.

Concepts of scale in simulated annealing

Abstract: Simulated annealing is a powerful technique for finding near‐optimal solutions to NP‐complete combinatorial optimization problems. In this technique, the states of a physical system are generalized to states of a system being optimized, the physical energy is generalized to the function being minimized, and the temperature is generalized to a control parameter for the optimization process. Wire length minimization in circuit placement is used as an example to show how ideas from statistical physics can elucidate the annealing process. The mean of the distribution of states in energy is a maximum energy scale of the system, its standard deviation defines the maximum temperature scale, and the minimum change in energy defines the minimum temperature scale. These temperature scales tell us where to begin and end an annealing schedule. The ‘‘size’’ of a class of moves within the state space of the system is defined as the average change in the energy induced by moves of that class. These move scales are related to the characteristic temperature scales of a system, and show that a move class should be used when it gives an average change in energy on the order of the temperature. This, in turn, helps improve the performance of the algorithm.

A Novel Coding Scheme for Practical Economic Dispatch by Modified Particle Swarm Approach

Abstract: This paper proposes a new approach and coding scheme for solving economic dispatch problems in power systems through simulated annealing like particle swarm optimization (SA-PSO). This novel coding scheme could effectively prevent obtaining infeasible solutions through the application of stochastic search methods, thereby dramatically improving search efficiency and solution quality. Many nonlinear characteristics of power generators, and their operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones, transmission loss, and nonlinear cost functions, were all considered for practical operation. The effectiveness and feasibility of the proposed method were demonstrated by four system case studies and compared with previous literature in terms of solution quality and computational efficiency. The experiment showed encouraging results, suggesting that the proposed approach was capable of efficiently determining higher quality solutions addressing economic dispatch problems.

Localization in wireless sensor networks using particle swarm optimization

Abstract: This paper proposes a novel and computationally efficient global optimization method based on swarm intelligence for locating nodes in a WSN environment The mean squared range error of all neighbouring anchor nodes is taken as the objective function for this non linear optimization problem The Particle Swarm Optimization (PSO) is a high performance stochastic global optimization tool that ensures the minimization of the objective function, without being trapped into local optima The easy implementation and low memory requirement features of PSO make it suitable for highly resource constrained WSN environments Computational experiments on data drawn from simulated WSNs show better convergence characteristics than the existing Simulated Annealing based WSN localization

Particle swarm optimization technique based short-term hydrothermal scheduling

Abstract: Particle swarm optimization is applied to determine the optimal hourly schedule of power generation in a hydrothermal power system. A multi-reservoir cascaded hydroelectric system with a nonlinear relationship between water discharge rate, net head and power generation is considered. The water transport delay between connected reservoirs is taken into account. In the present work, the effects of valve point loading in the fuel cost function of the thermal plants are also taken into consideration. The developed algorithm is illustrated for a test system consisting of four hydro plants and three thermal plants. Cost characteristics of individual thermal units are considered. The test results are compared with those obtained using evolutionary programming and simulated annealing technique. It is found that the convergence characteristic is excellent and the results obtained by the proposed method are superior in terms of fuel cost and computation time.

Automated neuron model optimization techniques: a review

Abstract: The increase in complexity of computational neuron models makes the hand tuning of model parameters more difficult than ever. Fortunately, the parallel increase in computer power allows scientists to automate this tuning. Optimization algorithms need two essential components. The first one is a function that measures the difference between the output of the model with a given set of parameter and the data. This error function or fitness function makes the ranking of different parameter sets possible. The second component is a search algorithm that explores the parameter space to find the best parameter set in a minimal amount of time. In this review we distinguish three types of error functions: feature-based ones, point-by-point comparison of voltage traces and multi-objective functions. We then detail several popular search algorithms, including brute-force methods, simulated annealing, genetic algorithms, evolution strategies, differential evolution and particle-swarm optimization. Last, we shortly describe Neurofitter, a free software package that combines a phase–plane trajectory density fitness function with several search algorithms.

Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques

Abstract: In this paper, we present a new method for temperature prediction and the Taiwan Futures Exchange (TAIFEX) forecasting, based on high-order fuzzy logical relationships and genetic simulated annealing techniques, where simulated annealing techniques are used to deal with mutation operations of genetic algorithms. We use genetic simulated annealing techniques to adjust the length of each interval in the universe of discourse to increase the forecasting accuracy rate. The proposed method gets higher forecasting accuracy rates than the existing methods.

Optimal Planning of Radial Distribution Networks by Simulated Annealing Technique

Abstract: A method for optimal planning of radial distribution networks is presented in detail based upon a combination of the steepest descent and the simulated annealing approaches. The object of investigation is the complete network of available routes and the optimization goal is to find the routes that provide the minimal total annual cost. The minimum capital cost oriented solution created by applying the steepest descent approach is used as the initial solution for the optimization procedure that is further improved by simulated annealing to obtain the minimum total cost solution. The method takes into account the capital recovery, energy loss and undelivered energy costs.

Multiobjective Optimization of Concrete Frames by Simulated Annealing

Abstract: This article aims to describe a methodology to design RC building frames based on a multiobjective simulated annealing (MOSA) algorithm applied to four objective functions, namely, the economic cost, the constructability, the environmental impact, and the overall safety of RC framed structures. The evaluation of solutions follows the Spanish Code for structural concrete. The methodology was applied to a symmetrical building frame with two bays and four floors. This example has 77 design variables. Pareto results of the MOSA algorithm indicate that more practical, more constructable, more sustainable, and safer solutions than the lowest cost solution are available at a cost increment acceptable in practice. Results N s -SMOSA1 and N s -SMOSA2 of the cost versus constructability Pareto front are finally recommended because they are especially good in terms of cost, constructability, and environmental impact. Further, the methodology proposed will help structural engineers to enhance their designs of building frames.

A simulated annealing algorithm for manufacturing cell formation problems

Abstract: The cell formation problem determines the decomposition of the manufacturing cells of a production system in which machines are assigned to these cells to process one or more part families so that each cell is operated independently and the intercellular movements are minimized or the number of parts flow processed within cells is maximized. In this study, a simple yet effective simulated annealing-based approach, SACF, is proposed to solve the cell formation problem. Considerable efforts are devoted to the design of parts and machine assignment procedures to direct SACF to converge to solutions with good values of grouping efficacy. A set of 25 test problems with various sizes drawn from the literature is used to test the performance of the proposed heuristic algorithm. The corresponding results are compared to several well-known algorithms published. The comparative study shows that the proposed SACF algorithm improves the grouping efficacy for 72% of the test problems. The proposed algorithm should thus be useful to both practitioners and researchers.

An enhanced memetic differential evolution in filter design for defect detection in paper production

Abstract: This article proposes an Enhanced Memetic Differential Evolution (EMDE) for designing digital filters which aim at detecting defects of the paper produced during an industrial process. Defect detection is handled by means of two Gabor filters and their design is performed by the EMDE. The EMDE is a novel adaptive evolutionary algorithm which combines the powerful explorative features of Differential Evolution with the exploitative features of three local search algorithms employing different pivot rules and neighborhood generating functions. These local search algorithms are the Hooke Jeeves Algorithm, a Stochastic Local Search, and Simulated Annealing. The local search algorithms are adaptively coordinated by means of a control parameter that measures fitness distribution among individuals of the population and a novel probabilistic scheme. Numerical results confirm that Differential Evolution is an efficient evolutionary framework for the image processing problem under investigation and show that the EMDE performs well. As a matter of fact, the application of the EMDE leads to a design of an efficiently tailored filter. A comparison with various popular metaheuristics proves the effectiveness of the EMDE in terms of convergence speed, stagnation prevention, and capability in detecting solutions having high performance.

Dominance-Based Multiobjective Simulated Annealing

Abstract: Simulated annealing is a provably convergent optimizer for single-objective problems. Previously proposed multiobjective extensions have mostly taken the form of a single-objective simulated annealer optimizing a composite function of the objectives. We propose a multiobjective simulated annealer utilizing the relative dominance of a solution as the system energy for optimization, eliminating problems associated with composite objective functions. We also propose a method for choosing perturbation scalings promoting search both towards and across the Pareto front. We illustrate the simulated annealer's performance on a suite of standard test problems and provide comparisons with another multiobjective simulated annealer and the NSGA-II genetic algorithm. The new simulated annealer is shown to promote rapid convergence to the true Pareto front with a good coverage of solutions across it comparing favorably with the other algorithms. An application of the simulated annealer to an industrial problem, the optimization of a code-division-multiple access (CDMA) mobile telecommunications network's air interface, is presented and the simulated annealer is shown to generate nondominated solutions with an even and dense coverage that outperforms single objective genetic algorithm optimizers.

Optimizing Product Line Designs: Efficient Methods and Comparisons

Abstract: We take advantage of recent advances in optimization methods and computer hardware to identify globally optimal solutions of product line design problems that are too large for complete enumeration. We then use this guarantee of global optimality to benchmark the performance of more practical heuristic methods. We use two sources of data: (1) a conjoint study previously conducted for a real product line design problem, and (2) simulated problems of various sizes. For both data sources, several of the heuristic methods consistently find optimal or near-optimal solutions, including simulated annealing, divide-and-conquer, product-swapping, and genetic algorithms.

Quantum-Inspired Evolutionary Algorithm for Real and Reactive Power Dispatch

Abstract: This paper presents an evolutionary algorithm based on quantum computation for bid-based optimal real and reactive power (P-Q) dispatch. The proposed quantum-inspired evolutionary algorithm (QEA) has applications in various combinatorial optimization problems in power systems and elsewhere. In this paper, the QEA determines the settings of control variables, such as generator outputs, generator voltages, transformer taps and shunt VAR compensation devices for optimal P-Q dispatch considering the bid-offered cost. The algorithm is tested on the IEEE 30-bus system, and the results obtained by the QEA are compared with those obtained by other modern heuristic techniques: ant colony system (ACS), enhanced GA and simulated annealing (SA) as well as the original QEA. Furthermore, in order to demonstrate the applicability of the proposed QEA, it is also implemented in a different problem, which is to minimize the real power losses in the IEEE 118-bus transmission system. The comparisons demonstrate an improved performance of the proposed QEA.

Cluster-based Simulated Annealing for Mapping Cores onto 2D Mesh Networks on Chip

Abstract: In network-on-chip (NoC) application design, core-to-node mapping is an important but intractable optimization problem. In the paper, we use simulated annealing to tackle the mapping problem in 2D mesh NoCs. In particular, we combine a clustering technique with the simulated annealing to speed up the convergence to near-optimal solutions. The clustering exploits the connectivity and distance relation in the network architecture as well as the locality and bandwidth requirements in the core communication graph. The annealing is cluster-aware and may be dynamically constrained within clusters. Our experiments suggest that simulated annealing can be effectively used to solve the mapping problem with a scalable size, and the combined strategy improves over the simulated annealing in execution time by up to 30% without compromising the quality of solutions.