Performance Of Simulated Annealing Research Articles

Quantum and classical annealing in a continuous space with multiple local minima

The protocol of quantum annealing is applied to an optimization problem with a one-dimensional continuous degree of freedom, a variant of the problem proposed by Shinomoto and Kabashima. The energy landscape has a number of local minima, and the classical approach of simulated annealing is predicted to have a logarithmically slow convergence to the global minimum. We show by extensive numerical analyses that quantum annealing yields a power-law convergence, thus an exponential improvement over simulated annealing. The power is larger, and thus the convergence is faster, than a prediction by an existing phenomenological theory for this problem. Performance of simulated annealing is shown to be enhanced by introducing quasiglobal searches across energy barriers, leading to a power-law convergence but with a smaller power than in the quantum case and thus a slower convergence classically even with quasiglobal search processes. We also reveal how diabatic quantum dynamics, quantum tunneling in particular, steers the systems toward the global minimum by a meticulous choice of annealing schedule. This latter result explicitly contrasts the role of tunneling in quantum annealing against the classical counterpart of stochastic optimization by simulated annealing.

Performance of Multicriteria Evaluation and Heuristic Methods in the Delineation of Green Infrastructure in Areas with Fragmented Landscapes

The EU Commission has established Green infrastructure as one of the tools to preserve biodiversity and grant the provision of ecosystem services that reduce impacts on natural values like those produced by climate change. Therefore, a European green infrastructure strategy has been created that commit member states to incorporate green infrastructure to their territorial planning. Yet, methodologies to delimit green infrastructure so as to facilitate its inclusion in territorial plans are still scarce. The available methods are mainly based in multicriteria evaluation and focus on zoning general green infrastructure areas taking into account the provision potential of just a few ecosystem services. Considering the provision of a wide range of ecosystem services to delimit green infrastructure elements is key to grant their multifunctionality and increase their efficiency mitigating climate change impacts in natural values and human population. However, the lack of data or the high cost to accurately map ecosystem services provision potential, leads most of the time to infer it from land cover data. This creates problems when using these maps to delimit green infrastructure in areas with fragmented landscapes; since identified green infrastructure areas may be irregular and scattered. There are heuristic methods like simulated annealing that have been used to identify ecosystem services hot spots which consider the regularity and size of the identified patches. These methods can be used to delimit green infrastructure in fragmented landscapes finding a balance between the regularity of the areas and their potential to provide multiple ecosystem services. In the current work, a comparison has been made between the performance of simulated annealing and current multicriteria evaluation methods to delimit green infrastructure multifunctional buffer zones in an area of north-western Spain with a very fragmented landscape. Results have shown that simulated annealing delimits more regular multifunctional buffer areas but with a less average potential for providing multiple ecosystem services. The conclusions of the paper indicate that simulated annealing is good produces more regular multifunctional areas but with a lower ESs provision potential. It was observed that in the case of ESs that were mapped considering factors at landscape scale, their provision potential did not vary too much between the multifunctional buffer areas delimited with each of the methods. This indicates that delineation methods may produce more regular GI elements if ESs provision potential is mapped considering the influence of biophysical factors at a wider landscape scale.

Appropriate search techniques to estimate Weibull function parameters in a Pinus spp. plantation

The Weibull function, a continuous probability distribution, is widely used for diameter distribution modelling, in which parameter estimation performance is affected by stand attributes and fitting methods. The Weibull cumulative distribution function is nonlinear, and classical fitting methods may provide a not optimal solution. Invoking the use of artificial intelligence by metaheuristics is reasonable for this optimisation task. Therefore, aimed and compared (1) the metaheuristics genetic algorithm and simulated annealing performance over the moment and percentile methods; (2) the hybrid strategy merging the metaheuristics tested and the percentile method and, (3) the metaheuristics fitness functions under basal area and density errors. A long-term experiment in a Pinus taeda stand subjected to crown thinning was used. According to our findings, all methods have a similar performance, independent of the thinning regimes and age. The pattern of the estimated parameters tends to be acceptable, as b increases over time and c increases after thinning. Overall, our findings suggest that methods based on metaheuristics have a higher precision than classical methods for estimating Weibull parameters. According to the classification test, the methods that involved simulated annealing stood out. The hybrid method involving this metaheuristic also stood out, with accurate estimates. Classical methods showed the poorest performance, and we therefore suggest the use of simulated annealing due to its faster processing time and high-quality solution.

Multicompare tests of the performance of different metaheuristics in EEG dipole source localization.

We study the use of nonparametric multicompare statistical tests on the performance of simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), when used for electroencephalographic (EEG) source localization. Such task can be posed as an optimization problem for which the referred metaheuristic methods are well suited. Hence, we evaluate the localization's performance in terms of metaheuristics' operational parameters and for a fixed number of evaluations of the objective function. In this way, we are able to link the efficiency of the metaheuristics with a common measure of computational cost. Our results did not show significant differences in the metaheuristics' performance for the case of single source localization. In case of localizing two correlated sources, we found that PSO (ring and tree topologies) and DE performed the worst, then they should not be considered in large-scale EEG source localization problems. Overall, the multicompare tests allowed to demonstrate the little effect that the selection of a particular metaheuristic and the variations in their operational parameters have in this optimization problem.

Performance of simulated annealing in p-spin glasses

We perform careful numerical simulations of slow Monte-Carlo annealings in the dense 3-body spin glass model and compare with the predictions from different theories: thresholds states, isocomplexity, following state. We conclude that while isocomplexity and following state both provide excellent agreement the numerical data, the influence of threshold states – that is still the most commonly considered theory – can be excluded from our data.

Estimating Kinetic Parameters for Essential Amino Acid Production in Arabidopsis Thaliana by Using Particle Swarm Optimization

Parameter estimation is one of nine phases in modelling, which is the most challenging task that is used to estimate the parameter values for biological system that is non-linear. There is no general solution for determining the nonlinearity of the dynamic model. Experimental measurement is expensive, hard and time consuming. Hence, the aim for this research is to implement Particle Swarm Optimization (PSO) intoSBToolbox to solve the mentioned problems. As a result, the optimum kinetic parameters for simulating essential amino acid metabolism in plant model Arabidopsis Thaliana are obtained. There are four performance measurements used, namely computational time, average of error rate, standard deviation and production of graph. As a finding of this research, PSO has the smallest standard deviation and average of error rate. The computational time in parameter estimation is smaller in comparison with others, indicating that PSO is a consistent method to estimate parameter values compared to the performance of Simulated Annealing (SA) and downhill simplex method after the implementation into SBToolbox.

OPTIMAL FEATURE SET AND PARAMETER SELECTION FOR POWER QUALITY DATA MINING

The significance of detection and classification of power quality (PQ) events that disturb the voltage and/or current waveforms in electrical power distribution networks is well known. Consequently, in spite of a large number of research reports in this area, research on the selection of useful features from the existing feature set and the parameter selection for specific classifiers has thus far not been explored. The choice of a smoothing parameter for a probabilistic neural network classifier (PNN) in the training process, together with feature selection, will significantly impact the classification accuracy. In this work, a thorough analysis is carried out, using two wrapper-based optimization techniques—the genetic algorithm and simulated annealing—for identifying the ensemble of celebrated features obtained using discrete wavelet transform together with the smoothing parameter selection of the PNN classifier. As a result of these analyses, the proper smoothing parameter together with a more useful feature set from among a wider set of features for the PNN classifier is obtained with improved classification accuracy. Furthermore, the results show that the performance of simulated annealing is better than the genetic algorithm for feature selection and parameter optimization in Power Quality Data Mining.

Optimizing the Radio Network Parameters of the Long Term Evolution System Using Taguchi's Method

One of the primary aims of radio network planning is to configure the parameters of the base stations such that the deployment achieves the required quality of service. However, the adjustment of radio network parameters in a heterogeneous macro-only cellular network is a complex task, which involves a large number of configuration parameters with interactions among them. Existing commercial planning tools are based on local search methods, e.g., simulated annealing, that require problem-specific and heuristic definitions of the input parameters. The problem with local search methods is that their performance can significantly be degraded if the input parameters are misconfigured. To overcome these difficulties, an iterative optimization procedure based on Taguchi's method is proposed to find near-optimal settings. Taguchi's method was originally applied in manufacturing processes and has recently been used in several engineering fields. Unlike local search methods that heuristically discover the multidimensional parameter space of candidate solutions, Taguchi's method offers a scientifically disciplined methodology to explore the search space and select near-optimal values for the parameters. In this paper, the application of Taguchi's method in radio network optimization is illustrated by setting typical radio network parameters of the Long Term Evolution (LTE) system, i.e., the uplink power control parameters, antenna tilts, and azimuth orientations of trisectored macro base stations. Simulation results reveal that Taguchi's method is a promising approach for radio network optimization with respect to performance and computational complexity. It is shown that Taguchi's method has a comparable performance to simulated annealing in terms of power control and antenna azimuth optimizations; however, it performs better in terms of antenna tilt optimization. Moreover, it is presented that the performance of simulated annealing, as opposed to Taguchi's method, highly depends on the definition of the input parameters.

Optimisation algorithms for spatially constrained forest planning

We compared genetic algorithms, simulated annealing and hill climbing algorithms on spatially constrained, integrated forest planning problems. There has been growing interest in algorithms that mimic natural processes, such as genetic algorithms and simulated annealing. These algorithms use random moves to generate new solutions, and employ a probabilistic acceptance/rejection criterion that allows inferior moves within the search space. Algorithms for a genetic algorithm, simulated annealing, and random hill climbing are formulated and tested on a same-sample forest-planning problem where the adjacency rule is strictly enforced. Each method was randomly started 20 times and allowed to run for 10,000 iterations. All three algorithms identified good solutions (within 3% of the highest found), however, simulated annealing consistently produced superior solutions. Simulated annealing and random hill climbing were approximately 10 times faster than the genetic algorithm because only one solution needs to be modified at each iteration. Performance of simulated annealing was essentially independent of the starting point, giving it an important advantage over random hill climbing. The genetic algorithm was not well suited to the strict adjacency problem because considerable computation time was necessary to repair the damage caused during crossover.

Nonlinear kinetic parameter estimation using simulated annealing

The performance of simulated annealing (S-A) in nonlinear kinetic parameter estimation was studied and compared with the classical Levenberg–Marquardt (L–M) algorithm. Both methods were tested in the estimation of kinetic parameters using a set of three kinetic models of progressively higher complexity. The models describe the catalytic wet air oxidation of phenol carried out in a small-scale trickle bed reactor. The first model only considered the phenol disappearance reaction, while the other two included oxidation intermediate compounds. The number of model parameters involved increased from 3 to 23 and 38, respectively, for the three models. Both algorithms gave good results for the first model, although the L–M was superior in terms of computation time. In the second case the algorithms achieved convergence, but S-A resulted in a better criterion and kinetic parameters with physical meaning. In the more complex model, only S-A was capable of achieving convergence, whereas the L–M failed. For the second and third model the solution of S-A could be further improved, when used as an initial guess for the L–M algorithm.

Problem space local search for number partitioning

We show how simple and effective metaheuristics can be developed for the number partitioning problem using the “problem space” approach [1]. In a previous application of local search to number partitioning [2], it was found that the performance of simulated annealing used in conjunction with “swap neighborhoods” was disappointing relative to the differencing heuristic of Karmarkar and Karp [3]. Using problem space neighborhoods as an alternative to swapping, we empirically demonstrate several orders of magnitude improvement over the differencing algorithm, albeit with greater computation time. This improvement in performance comes as little surprise since a modified version of the differencing heuristic is explicitly embedded in the problem space algorithm.

Kalman filter quantitative resolution of shifted ESCA spectra after optimal alignment by simulated annealing

The quantitative separation of overlapped responses accomplished by the Kalman filter algorithm is deteriorated by the errors in the model due to shifts of the component peaks. This is particularly true for spectral resolution in Electron Spectroscopy for Chemical Analysis (ESCA). A computational approach for the quantitative resolution of overlapping ESCA spectra when there is loss of collinearity between the pure component peaks and the mixture peak, has been developed. The procedure makes itera tive use of the Kalman filter for resolving the mixture spectrum with the component spectra aligned according to some values of the position parameters, and of the simulated annealing algorithm to find the optimal alignment. The performance of simulated annealing in pursuing this task, has been compared with those of simplex and steepest descent, by analyzing a set of overlapped synthetic spectra and some ESCA spectra of powder mixtures of lead compounds.

Good solutions to discrete noxious location problems via metaheuristics

An implementation of simulated annealing and tabu search is described for discrete versions of two noxious facility location problems —p-dispersion andp-defense-sum. A series of computational experiments leading to good choices of the parameters that drive simulated annealing and tabu search are presented for a 33-node data set. Using these parameter settings, the performance of simulated annealing and tabu search are compared to a semi-greedy heuristic for thirty randomly generated 25-node data sets.

Manufacturing cell formation: A dual-objective simulated annealing approach

A dual-objective simulated annealing approach is developed for the purpose of concurrently forming machine cells and part families for cellular manufacturing. Simulated annealing has proved to be very effective in handling a variety of NP-hard problems. Originally, the simulated annealing algorithm was designed for applications with a single objective. For forming machine cells based on a machine-component incidence matrix, the result from single-objective simulated annealing was found to have room for improvement. In order to improve the performance of simulated annealing, a dual-objective approach is developed. The result clearly demonstrates the oustanding capabilities of the dual-objective approach. The discussions are illustrated with examples.